Models | Description | |

1. | Burst and tonic firing behaviour in subfornical organ (SFO) neurons (Medlock et al 2018) | |

"Subfornical organ (SFO) neurons exhibit heterogeneity in current expression and spiking behavior, where the two major spiking phenotypes appear as tonic and burst firing. Insight into the mechanisms behind this heterogeneity is critical for understanding how the SFO, a sensory circumventricular organ, integrates and selectively influences physiological function. To integrate efficient methods for studying this heterogeneity, we built a single-compartment, Hodgkin-Huxley-type model of an SFO neuron that is parameterized by SFO-specific in vitro patch-clamp data. The model accounts for the membrane potential distribution and spike train variability of both tonic and burst firing SFO neurons. Analysis of model dynamics confirms that a persistent Na+ and Ca2+ currents are required for burst initiation and maintenance and suggests that a slow-activating K+ current may be responsible for burst termination in SFO neurons. Additionally, the model suggests that heterogeneity in current expression and subsequent influence on spike afterpotential underlie the behavioral differences between tonic and burst firing SFO neurons. Future use of this model in coordination with single neuron patch-clamp electrophysiology provides a platform for explaining and predicting the response of SFO neurons to various combinations of circulating signals, thus elucidating the mechanisms underlying physiological signal integration within the SFO." | ||

2. | A basal ganglia model of aberrant learning (Ursino et al. 2018) | |

A comprehensive, biologically inspired neurocomputational model of action selection in the Basal Ganglia allows simulation of dopamine induced aberrant learning in Parkinsonian subjects. In particular, the model simulates the Alternate Finger Tapping motor task as an indicator of bradykinesia. | ||

3. | A bistable model of Spike-Wave seizure and background activity (Taylor et al. 2014) | |

This is a four-variable model (in the Amari formalism) of bistable Spike-Wave seizure dynamics and background activity (fixed point). The published code is the deterministic version of the model in the related publication. This model can be used to investigate seizure abatement using stimulation. | ||

4. | A Computational Model for the Binocular Vector Disparity Estimation (Chessa & Solari 2018) | |

A biologically-inspired model of disparity estimation: we consider the disparity patterns that arise when artificial and living beings fixate objects in the surrounding environment, in these situations the disparity is a vector quantity (i.e. vertical and horizontal disparities). | ||

5. | A computational model of a small DRG neuron to explore pain (Verma et al. 2019, 2020) | |

This is a Hodgkin-Huxley type model for a small DRG neuron consisting of four voltage-gated ion channels: sodium channels 1.7 and 1.8, delayed rectifier potassium, and A-type transient potassium channels. This model was used to explore the dynamics of this neuron using bifurcation theory, with the motive to investigate pain since small DRG neuron is a pain-sensing neuron. | ||

6. | A computational model of action selection in the basal ganglia (Suryanarayana et al 2019) | |

" ... Here, we incorporate newly revealed subgroups of neurons within the GPe into an existing computational model of the basal ganglia, and investigate their role in action selection. Three main results ensued. First, using previously used metrics for selection, the new extended connectivity improved the action selection performance of the model. Second, low frequency theta oscillations were observed in the subpopulation of the GPe (the TA or ‘arkypallidal’ neurons) which project exclusively to the striatum. These oscillations were suppressed by increased dopamine activity — revealing a possible link with symptoms of Parkinson’s disease. Third, a new phenomenon was observed in which the usual monotonic relationship between input to the basal ganglia and its output within an action ‘channel’ was, under some circumstances, reversed. ..." | ||

7. | A computational model of single-neuron perturbations (Sadeh and Clopath 2020) | |

A computational model to study the effect of single-neuron perturbations in large-scale excitatory-inhibitory networks of the primary visual cortex. Neuronal receptive fields and connectivity are constrained by experimental literature. The model addresses how the influence of perturbing an excitatory neuron ("influencer") in the network on other neurons ("influencees") depends on the similarity of their receptive fields. Specifically, in which regimes this influence is dominated by amplification or suppression, and how it relates to functional properties of neurons. | ||

8. | A cortical sheet mesoscopic model for investigating focal seizure onset dynamics (Wang et al. 2014) | |

The model uses realistically coupled, discretised, Wilson-Cowan units to describe the spatio-temporal activity of a cortical sheet. This model has been used the investigate the dynamic onset mechanisms of focal seizures. | ||

9. | A detailed data-driven network model of prefrontal cortex (Hass et al 2016) | |

Data-based PFC-like circuit with layer 2/3 and 5, synaptic clustering, four types of interneurons and cell-type specific short-term synaptic plasticity; neuron parameters fitted to in vitro data, all other parameters constrained by experimental literature. Reproduces key features of in vivo resting state activity without specific tuning. | ||

10. | A generic MAPK cascade model for random parameter sampling analysis (Mai and Liu 2013) | |

A generic three-tier MAPK cascade model constructed by comparing previous MAPK models covering a range of biosystems. Pseudo parameters and random sampling were employed for qualitative analysis. A range of kinetic behaviors of MAPK activation, including ultrasensitivity, bistability, transient activation and oscillation, were successfully reproduced in this generic model. The mechanisms were revealed by statistic analysis of the parameter sets. | ||

11. | A kinetic model unifying presynaptic short-term facilitation and depression (Lee et al. 2009) | |

"... Here, we propose a unified theory of synaptic short-term plasticity based on realistic yet tractable and testable model descriptions of the underlying intracellular biochemical processes. Analysis of the model equations leads to a closed-form solution of the resonance frequency, a function of several critical biophysical parameters, as the single key indicator of the propensity for synaptic facilitation or depression under repetitive stimuli. This integrative model is supported by a broad range of transient and frequency response experimental data including those from facilitating, depressing or mixed-mode synapses. ... the model provides the reasons behind the switching behavior between facilitation and depression observed in experiments. ..." | ||

12. | A mathematical model of a neurovascular unit (Dormanns et al 2015, 2016) (Farrs & David 2011) | |

Here a lumped parameter numerical model of a neurovascular unit is presented, representing an intercellular communication system based on ion exchange through pumps and channels between neurons, astrocytes, smooth muscle cells, endothelial cells, and the spaces between these cells: the synaptic cleft between the neuron and astrocyte, the perivascular space between the astrocyte and SMC, and the extracellular space surrounding the cells. The model contains various cellular and chemical pathways such as potassium, astrocytic calcium, and nitric oxide. The model is able to simulate neurovascular coupling, the process characterised by an increase in neuronal activity followed by a rapid dilation of local blood vessels and hence increased blood supply providing oxygen and glucose to cells in need. | ||

13. | A mathematical model of evoked calcium dynamics in astrocytes (Handy et al 2017) | |

" ...Here we present a qualitative analysis of a recent mathematical model of astrocyte calcium responses. We show how the major response types are generated in the model as a result of the underlying bifurcation structure. By varying key channel parameters, mimicking blockers used by experimentalists, we manipulate this underlying bifurcation structure and predict how the distributions of responses can change. We find that store-operated calcium channels, plasma membrane bound channels with little activity during calcium transients, have a surprisingly strong effect, underscoring the importance of considering these channels in both experiments and mathematical settings. ..." | ||

14. | A microcircuit model of the frontal eye fields (Heinzle et al. 2007) | |

" ... we show that the canonical circuit (Douglas et al. 1989, Douglas and Martin 1991) can, with a few modifications, model the primate FEF. The spike-based network of integrate-and-fire neurons was tested in tasks that were used in electrophysiological experiments in behaving macaque monkeys. The dynamics of the model matched those of neurons observed in the FEF, and the behavioral results matched those observed in psychophysical experiments. The close relationship between the model and the cortical architecture allows a detailed comparison of the simulation results with physiological data and predicts details of the anatomical circuit of the FEF." | ||

15. | A model of local field potentials generated by medial superior olive neurons (Goldwyn et al 2014) | |

A computational model of local field potentials generated by medial superior olive neurons. These field potentials are known as the "auditory neurophonic". MSO neuron is modeled as a soma and two dendrites (following Mathews et al, Nature Neurosci, 2010). Intracellular and a 1D extracellular domain are dynamically coupled and solved to simulate spatial-temporal patterns of membrane voltage and extracellular voltage in response to trains of synaptic inputs (monolateral or bilateral, excitation and/or inhibition). The model produces spatio-temporal patterns similar to neurophonic responses recorded in vivo, as discussed in the accompanying manuscript. | ||

16. | A model of neurovascular coupling and the BOLD response (Mathias et al 2017, Kenny et al 2018) | |

Here a lumped parameter numerical model of a neurovascular unit is presented, representing an intercellular communication system based on ion exchange through pumps and channels between neurons, astrocytes, smooth muscle cells, endothelial cells, and the spaces between these cells: the synaptic cleft between the neuron and astrocyte, the perivascular space between the astrocyte and SMC, and the extracellular space surrounding the cells. The model contains various cellular and chemical pathways such as potassium, astrocytic calcium, and nitric oxide. The model is able to simulate neurovascular coupling, the process characterised by an increase in neuronal activity followed by a rapid dilation of local blood vessels and hence increased blood supply providing oxygen and glucose to cells in need. The model also incorporates the BOLD response. | ||

17. | A multiphysics neuron model for cellular volume dynamics (Lee et al. 2011) | |

This paper introduces a novel neuron model, where the cell volume is a time-varying variable and multiple physical principles are combined to build governing equations. Using this model, we analyzed neuronal volume responses during excitation, which elucidated the waveforms of fast intrinsic optical signals observed experimentally across the literature. In addition, we analyzed volume responses on a longer time scale with repetitive stimulation to study the characteristics of slow cell swelling. | ||

18. | A Neural mass computational model of the Thalamocorticothalamic circuitry (Bhattacharya et al. 2011) | |

The model presented here is a bio-physically plausible version of a simple thalamo-cortical neural mass computational model proposed by Lopes da Silva in 1974 to simulate brain EEG activity within the alpha band (8-13 Hz). The thalamic and cortical circuitry are presented as separate modules in this model with cell populations as in biology. The connectivity between cell populations are as reported by Sherman, S. in Scholarpedia, 2006. The values of the synaptic connectivity parameters are as reported by Van Horn et al, 2000. In our paper (doi:10.1016/j.neunet.2011.02.009), we study the model behaviour while varying the values of the synaptic connectivity parameters (Cyyy) in the model about their respective 'basal' (intial) values. | ||

19. | A neural mass model for critical assessment of brain connectivity (Ursino et al 2020) | |

We use a neural mass model of interconnected regions of interest to simulate reliable neuroelectrical signals in the cortex. In particular, signals simulating mean field potentials were generated assuming two, three or four ROIs, connected via excitatory or by-synaptic inhibitory links. Then we investigated whether bivariate Transfer Entropy (TE) can be used to detect a statistically significant connection from data (as in binary 0/1 networks), and even if connection strength can be quantified (i.e., the occurrence of a linear relationship between TE and connection strength). Results suggest that TE can reliably estimate the strength of connectivity if neural populations work in their linear regions. However, nonlinear phenomena dramatically affect the assessment of connectivity, since they may significantly reduce TE estimation. Software included here allows the simulation of neural mass models with a variable number of ROIs and connections, the estimation of TE using the free package Trentool, and the realization of figures to compare true connectivity with estimated values. | ||

20. | A neural model of Parkinson`s disease (Cutsuridis and Perantonis 2006, Cutsuridis 2006, 2007) | |

"A neural model of neuromodulatory (dopamine) control of arm movements in Parkinson’s disease (PD) bradykinesia was recently introduced [1, 2]. The model is multi-modular consisting of a basal ganglia module capable of selecting the most appropriate motor command in a given context, a cortical module for coordinating and executing the final motor commands, and a spino-musculo-skeletal module for guiding the arm to its final target and providing proprioceptive (feedback) input of the current state of the muscle and arm to higher cortical and lower spinal centers. ... The new (extended) model [3] predicted that the reduced reciprocal disynaptic Ia inhibition in the DA depleted case doesn’t lead to the co-contraction of antagonist motor units." See below readme and papers for more and details. | ||

21. | A neuronal circuit simulator for non Monte Carlo analysis of neuronal noise (Kilinc & Demir 2018) | |

cirsiumNeuron is a neuronal circuit simulator that can directly and efficiently compute characterizations of stochastic behavior, i.e., noise, for multi-neuron circuits. In cirsiumNeuron, we utilize a general modeling framework for biological neuronal circuits which systematically captures the nonstationary stochastic behavior of the ion channels and the synaptic processes. In this framework, we employ fine-grained, discrete-state, continuous-time Markov Chain (MC) models of both ion channels and synaptic processes in a unified manner. Our modeling framework can automatically generate the corresponding coarse-grained, continuous-state, continuous-time Stochastic Differential Equation (SDE) models. In addition, for the stochastic characterization of neuronal variability and noise, we have implemented semi-analytical, non Monte Carlo analysis techniques that work both in time and frequency domains, which were previously developed for analog electronic circuits. In these semi-analytical noise evaluation schemes, (differential) equations that directly govern probabilistic characterizations in the form of correlation functions (time domain) or spectral densities (frequency domain) are first derived analytically, and then solved numerically. These semi-analytical noise analysis techniques correctly and accurately capture the second order statistics (mean, variance, autocorrelation, and power spectral density) of the underlying neuronal processes as compared with Monte Carlo simulations. | ||

22. | A simulation method for the firing sequences of motor units (Jiang et al 2006) | |

" ... a novel model based on the Hodgkin–Huxley (HH) system is proposed, which has the ability to simulate the complex neurodynamics of the firing sequences of motor neurons. The model is presented at the cellular level and network level, and some simulation results from a simple 3-neuron network are presented to demonstrate its applications." See paper for more and details. | ||

23. | A spatially extended model for macroscopic spike-wave discharges (Taylor and Baier 2011) | |

A spatially extended neural field model for generating spike-wave based on the Amari (1977) model implemented in MATLAB. | ||

24. | A spiking model of cortical broadcast and competition (Shanahan 2008) | |

"This paper presents a computer model of cortical broadcast and competition based on spiking neurons and inspired by the hypothesis of a global neuronal workspace underlying conscious information processing in the human brain. In the model, the hypothesised workspace is realised by a collection of recurrently interconnected regions capable of sustaining and disseminating a reverberating spatial pattern of activation. ..." | ||

25. | A state-space model to quantify common input to motor neurons (Feeney et al 2017) | |

"... We introduce a space-state model in which the discharge activity of motor neurons is modeled as inhomogeneous Poisson processes and propose a method to quantify an abstract latent trajectory that represents the common input received by motor neurons. The approach also approximates the variation in synaptic noise in the common input signal. The model is validated with four data sets: a simulation of 120 motor units, a pair of integrate-and-fire neurons with a Renshaw cell providing inhibitory feedback, the discharge activity of 10 integrate-and-fire neurons, and the discharge times of concurrently active motor units during an isometric voluntary contraction. The simulations revealed that a latent state-space model is able to quantify the trajectory and variability of the common input signal across all four conditions. When compared with the cumulative spike train method of characterizing common input, the state-space approach was more sensitive to the details of the common input current and was less influenced by the duration of the signal. The state-space approach appears to be capable of detecting rather modest changes in common input signals across conditions." | ||

26. | A two-layer biophysical olfactory bulb model of cholinergic neuromodulation (Li and Cleland 2013) | |

This is a two-layer biophysical olfactory bulb (OB) network model to study cholinergic neuromodulation. Simulations show that nicotinic receptor activation sharpens mitral cell receptive field, while muscarinic receptor activation enhances network synchrony and gamma oscillations. This general model suggests that the roles of nicotinic and muscarinic receptors in OB are both distinct and complementary to one another, together regulating the effects of ascending cholinergic inputs on olfactory bulb transformations. | ||

27. | Acetylcholine-modulated plasticity in reward-driven navigation (Zannone et al 2018) | |

"Neuromodulation plays a fundamental role in the acquisition of new behaviours. In previous experimental work, we showed that acetylcholine biases hippocampal synaptic plasticity towards depression, and the subsequent application of dopamine can retroactively convert depression into potentiation. We also demonstrated that incorporating this sequentially neuromodulated Spike- Timing-Dependent Plasticity (STDP) rule in a network model of navigation yields effective learning of changing reward locations. Here, we employ computational modelling to further characterize the effects of cholinergic depression on behaviour. We find that acetylcholine, by allowing learning from negative outcomes, enhances exploration over the action space. We show that this results in a variety of effects, depending on the structure of the model, the environment and the task. Interestingly, sequentially neuromodulated STDP also yields flexible learning, surpassing the performance of other reward-modulated plasticity rules." | ||

28. | ACh modulation in olfactory bulb and piriform cortex (de Almeida et al. 2013;Devore S, et al. 2014) | |

This matlab code was used in the papers de Almeida, Idiart and Linster, (2013), Devore S, de Almeida L, Linster C (2014) . This work uses a computational model of the OB and PC and their common cholinergic inputs to investigate how bulbar cholinergic modulation affects cortical odor processing. | ||

29. | Activator protein 1(AP-1) transcriptional regulatory model in brainstem neurons (Makadia et al 2015) | |

We have developed a mathematical model of AT1R-activated signaling kinases and a downstream transcriptional regulatory network controlling the family of activator protein 1 (AP-1) transcription factors. The signaling interactions of the transcriptional model were modeled with either mass-action or Michaelis--Menten kinetics, whereas the phenomenological model of the kinases used exponentials. These models were validated against their respective data domains independently and were integrated into one. The model was implemented as a set of ordinary differential equations solved using the ode15s solver in Matlab (Mathworks, USA). | ||

30. | Activity dependent changes in dendritic spine density and spine structure (Crook et al. 2007) | |

"... In this work, we extend previous modeling studies [27] by combining a model for activity-dependent spine density with one for calcium-mediated spine stem restructuring. ... Additional equations characterize the change in spine density along the dendrite, the current balance equation for an individual spine head, the change in calcium concentration in the spine head, and the dynamics of spine stem resistance. We use computational studies to investigate the changes in spine density and structure for differing synaptic inputs and demonstrate the effects of these changes on the input-output properties of the dendritic branch. ... " | ||

31. | Adaptation of Short-Term Plasticity parameters (Esposito et al. 2015) | |

"The anatomical connectivity among neurons has been experimentally found to be largely non-random across brain areas. This means that certain connectivity motifs occur at a higher frequency than would be expected by chance. Of particular interest, short-term synaptic plasticity properties were found to colocalize with specific motifs: an over-expression of bidirectional motifs has been found in neuronal pairs where short-term facilitation dominates synaptic transmission among the neurons, whereas an over-expression of unidirectional motifs has been observed in neuronal pairs where short-term depression dominates. In previous work we found that, given a network with fixed short-term properties, the interaction between short- and long-term plasticity of synaptic transmission is sufficient for the emergence of specific motifs. Here, we introduce an error-driven learning mechanism for short-term plasticity that may explain how such observed correspondences develop from randomly initialized dynamic synapses. ..." | ||

32. | Ambiguous Encoding and Distorted Perception (Carlson and Kawasaki 2006) | |

"... In the weakly electric fish Eigenmannia, P- and T-type primary afferent fibers are specialized for encoding the amplitude and phase, respectively, of electrosensory stimuli. We used a stimulus estimation technique to quantify the ability of P- and T-units to encode random modulations in amplitude and phase. As expected, P-units exhibited a clear preference for encoding amplitude modulations, whereas T-units exhibited a clear preference for encoding phase modulations. Surprisingly, both types of afferents also encoded their nonpreferred stimulus attribute when it was presented in isolation or when the preferred stimulus attribute was sufficiently weak. Because afferent activity can be affected by modulations in either amplitude or phase, it is not possible to unambiguously distinguish between these two stimulus attributes by observing the activity of a single afferent fiber. Simple model neurons with a preference for encoding either amplitude or phase also encoded their nonpreferred stimulus attribute when it was presented in isolation, suggesting that such ambiguity is unavoidable. ... " See paper for more and details. | ||

33. | An oscillatory neural autoencoder based on frequency modulation and multiplexing (Soman et al 2018) | |

" ... We propose here an oscillatory neural network model that performs the function of an autoencoder. The model is a hybrid of rate-coded neurons and neural oscillators. Input signals modulate the frequency of the neural encoder oscillators. These signals are then multiplexed using a network of rate-code neurons that has afferent Hebbian and lateral anti-Hebbian connectivity, termed as Lateral Anti Hebbian Network (LAHN). Finally the LAHN output is de-multiplexed using an output neural layer which is a combination of adaptive Hopf and Kuramoto oscillators for the signal reconstruction. The Kuramoto-Hopf combination performing demodulation is a novel way of describing a neural phase-locked loop. The proposed model is tested using both synthetic signals and real world EEG signals. The proposed model arises out of the general motivation to construct biologically inspired, oscillatory versions of some of the standard neural network models, and presents itself as an autoencoder network based on oscillatory neurons applicable to time series signals. As a demonstration, the model is applied to compression of EEG signals." | ||

34. | Artificial neuron model (Izhikevich 2003, 2004, 2007) | |

A set of models is presented based on 2 related parameterizations to reproduce spiking and bursting behavior of multiple types of cortical neurons and thalamic neurons. These models combine the biologically plausibility of Hodgkin Huxley-type dynamics and the computational efficiency of integrate-and-fire neurons. Using these model, one can simulate tens of thousands of spiking cortical neurons in real time (1 ms resolution) using a desktop PC. | ||

35. | Auditory nerve model for predicting performance limits (Heinz et al 2001) | |

A computational auditory nerve (AN) model was developed for use in modeling psychophysical experiments with normal and impaired human listeners. In this phenomenological model, many physiologically vulnerable response properties associated with the cochlear amplifier are represented by a single nonlinear control mechanism, see paper for details. Several model versions are described that can be used to evaluate the relative effects of these nonlinear properties. | ||

36. | Auditory nerve model with linear tuning (Heinz et al 2001) | |

A method for calculating psychophysical performance limits based on stochastic neural responses is introduced and compared to previous analytical methods for evaluating auditory discrimination of tone frequency and level. The method uses signal detection theory and a computational model for a population of auditory nerve (AN) fiber responses. Please see paper for details. | ||

37. | Auditory nerve response model (Tan, Carney 2003) | |

A computational model was developed to simulate the responses of auditory-nerve (AN) fibers in cat. The incorporation of both the level-independent frequency glide and the level-dependent compressive nonlinearity into a phenomenological model for the AN was the primary focus of this work. The ability of this model to process arbitrary sound inputs makes it a useful tool for studying peripheral auditory processing. | ||

38. | Axon growth model (Diehl et al. 2016) | |

The model describes the elongation over time of an axon from a small neurite to its steady-state length. The elongation depends on the availability of tubulin dimers in the growth cone. The dimers are produced in the soma and then transported along the axon to the growth cone. Mathematically the model consists of a partial differential equation coupled with two nonlinear ordinary differential equations. The code implements a spatial scaling to deal with the growing (and shrinking) domain and a temporal scaling to deal with evolutions on different time scales. Further, the numerical scheme is chosen to fully utilize the structure of the problems. To summarize, this results in fast and reliable axon growth simulations. | ||

39. | Axonal HH-model for temperature stimulation (Fribance et al 2016) | |

"... To analyze the temperature effect, our study modified the classical HH axonal model by incorporating a membrane capacitance-temperature relationship. The modified model successfully simulated the generation and propagation of action potentials induced by a rapid increase in local temperature when the Curie temperature of membrane capacitance is below 40 °C, while the classical model failed to simulate the axonal excitation by temperature stimulation. The new model predicts that a rapid increase in local temperature produces a rapid increase in membrane capacitance, which causes an inward membrane current across the membrane capacitor strong enough to depolarize the membrane and generate an action potential. ..." | ||

40. | Balance of excitation and inhibition (Carvalho and Buonomano 2009) | |

" ... Here, theoretical analyses reveal that excitatory synaptic strength controls the threshold of the neuronal input-output function, while inhibitory plasticity alters the threshold and gain. Experimentally, changes in the balance of excitation and inhibition in CA1 pyramidal neurons also altered their input-output function as predicted by the model. These results support the existence of two functional modes of plasticity that can be used to optimize information processing: threshold and gain plasticity." | ||

41. | Basal Ganglia and Levodopa Pharmacodynamics model for parameter estimation in PD (Ursino et al 2020) | |

Parkinson disease (PD) is characterized by a clear beneficial motor response to levodopa (LD) treatment. However, with disease progression and longer LD exposure, drug-related motor fluctuations usually occur. Recognition of the individual relationship between LD concentration and its effect may be difficult, due to the complexity and variability of the mechanisms involved. This work proposes an innovative procedure for the automatic estimation of LD pharmacokinetics and pharmacodynamics parameters, by a biologically-inspired mathematical model. An original issue, compared with previous similar studies, is that the model comprises not only a compartmental description of LD pharmacokinetics in plasma and its effect on the striatal neurons, but also a neurocomputational model of basal ganglia action selection. Parameter estimation was achieved on 26 patients (13 with stable and 13 with fluctuating LD response) to mimic plasma LD concentration and alternate finger tapping frequency along four hours after LD administration, automatically minimizing a cost function of the difference between simulated and clinical data points. Results show that individual data can be satisfactorily simulated in all patients and that significant differences exist in the estimated parameters between the two groups. Specifically, the drug removal rate from the effect compartment, and the Hill coefficient of the concentration-effect relationship were significantly higher in the fluctuating than in the stable group. The model, with individualized parameters, may be used to reach a deeper comprehension of the PD mechanisms, mimic the effect of medication, and, based on the predicted neural responses, plan the correct management and design innovative therapeutic procedures. | ||

42. | Basal ganglia motor function and the inverse kinematics calculation (Salimi-Badr et al 2017) | |

The computational model to study the possible correlation between Basal Ganglia (BG) function and solving the Inverse Kinematics (IK). | ||

43. | Basal ganglia-corticothalamic (BGCT) network (Chen et al., 2014) | |

We developed a biophysical model of the basal ganglia-corticothalamic network in this work. "... We demonstrate that the typical absence seizure activities can be controlled and modulated by the direct GABAergic projections from the substantia nigra pars reticulata (SNr) to either the thalamic reticular nucleus (TRN) or the specific relay nuclei (SRN) of thalamus, through different biophysical mechanisms. ... results highlight the bidirectional functional roles of basal ganglia in controlling and modulating absence seizures, and might provide novel insights into the therapeutic treatments of this brain disorder." | ||

44. | Basal ganglia-thalamic network model for deep brain stimulation (So et al. 2012) | |

This is a model of the basal ganglia-thalamic network, modified from the Rubin and Terman model (High frequency stimulation of the Subthalamic Nucleus, Rubin and Terman 2004). We subsequently used this model to investigate the effectiveness of STN and GPi DBS as well as lesion when various proportions of local cells and fibers of passage were activated or silenced. The BG network exhibited characteristics consistent with published experimental data, both on the level of single cells and on the network level. Perhaps most notably, and in contrast to the original RT model, the changes in the thalamic error index with changes in the DBS frequency matched well the changes in clinical symptoms with changes in DBS frequency. | ||

45. | Basal ganglia-thalamocortical loop model of action selection (Humphries and Gurney 2002) | |

We embed our basal ganglia model into a wider circuit containing the motor thalamocortical loop and thalamic reticular nucleus (TRN). Simulation of this extended model showed that the additions gave five main results which are desirable in a selection/switching mechanism. First, low salience actions (i.e. those with low urgency) could be selected. Second, the range of salience values over which actions could be switched between was increased. Third, the contrast between the selected and non-selected actions was enhanced via improved differentiation of outputs from the BG. Fourth, transient increases in the salience of a non-selected action were prevented from interrupting the ongoing action, unless the transient was of sufficient magnitude. Finally, the selection of the ongoing action persisted when a new closely matched salience action became active. The first result was facilitated by the thalamocortical loop; the rest were dependent on the presence of the TRN. Thus, we conclude that the results are consistent with these structures having clearly defined functions in action selection. | ||

46. | Binocular energy model set for binocular neurons in optic lobe of praying mantis (Rosner et al 2019) | |

This is a version of the binocular energy model with parameters chosen to reproduce individual cells in praying mantis optic lobe. The receptive fields are very coarsely sampled (6 different horizontal locations only) to match the coarse sampling of the data given very limited recording time. | ||

47. | BK - CaV coupling (Montefusco et al. 2017) | |

An implementation of coupling between BK_Ca channels and CaV channels suitable for use in whole cell models. | ||

48. | Breakdown of accmmodation in nerve: a possible role for INAp (Hennings et al 2005) | |

The present modeling study suggests that persistent, low-threshold, rapidly activating sodium currents have a key role in breakdown of accommodation, and that breakdown of accommodation can be used as a tool for studying persistent sodium current under normal and pathological conditions. See paper for more and details. | ||

49. | Bump Attractor Models: Delayed Response & Recognition Span - spatial condition (Ibanez et al 2019) | |

The archive contains examples of two spatial working memory tasks: the Delayed Response Task (DRT) or oculomotor task & the Delayed Recognition Span Task in the spatial condition (DRSTsp). | ||

50. | Bursting and oscillations in RD1 Retina driven by AII Amacrine Neuron (Choi et al. 2014) | |

"In many forms of retinal degeneration, photoreceptors die but inner retinal circuits remain intact. In the rd1 mouse, an established model for blinding retinal diseases, spontaneous activity in the coupled network of AII amacrine and ON cone bipolar cells leads to rhythmic bursting of ganglion cells. Since such activity could impair retinal and/or cortical responses to restored photoreceptor function, understanding its nature is important for developing treatments of retinal pathologies. Here we analyzed a compartmental model of the wild-type mouse AII amacrine cell to predict that the cell's intrinsic membrane properties, specifically, interacting fast Na and slow, M-type K conductances, would allow its membrane potential to oscillate when light-evoked excitatory synaptic inputs were withdrawn following photoreceptor degeneration. ..." | ||

51. | CA1 pyramidal cells, basket cells, ripples (Malerba et al 2016) | |

Model of CA1 pyramidal layer Ripple activity, triggered when receiving current input (to represent CA3 sharp-waves). Cells are Adaptive-Exponential Integrate and Fire neurons, receiving independent OU noise. | ||

52. | CA1 pyramidal neuron dendritic spine with plasticity (O`Donnell et al. 2011) | |

Biophysical model of a dendritic spine and adjacent dendrite with synapse. Model parameters adjusted to fit CA3-CA1 Shaffer collateral synapse data from literature. Model includes both electrical and Ca2+ dynamics, including AMPARs, NMDARs, 4 types of CaV channel, and leak conductance. Spine and synapse are plastic according to Ca2+ dependent rule. The aim of the model is to explore the effects of dendritic spine structural plasticity on the rules of synaptic plasticity. | ||

53. | CA1 pyramidal: Stochastic amplification of KCa in Ca2+ microdomains (Stanley et al. 2011) | |

This minimal model investigates stochastic amplification of calcium-activated potassium (KCa) currents. Amplification results from calcium being released in short high amplitude pulses associated with the stochastic gating of calcium channels in microdomains. This model predicts that such pulsed release of calcium significantly increases subthreshold SK2 currents above what would be produced by standard deterministic models. However, there is little effect on a simple sAHP current kinetic scheme. This suggests that calcium stochasticity and microdomains should be considered when modeling certain KCa currents near subthreshold conditions. | ||

54. | CA3 Radiatum/Lacunosum-Moleculare interneuron, Ih (Anderson et al. 2011) | |

"The present study examines the biophysical properties and functional implications of Ih in hippocampal area CA3 interneurons with somata in strata radiatum and lacunosum-moleculare.... The functional consequences of Ih were examined with regard to temporal summation and impedance measurements. ... From impedance measurements, we found that Ih did not confer theta-band resonance, but flattened the impedance–frequency relations instead. ... Finally, a model of Ih was employed in computational analyses to confirm and elaborate upon the contributions of Ih to impedance and temporal summation." | ||

55. | Calyx of Held, short term plasticity (Yang Z et al. 2009) | |

This model investigates mechanisms contributing to short term plasticity at the calyx of Held, a giant glutamatergic synapse in the mammalian brainstem auditory system. It is a stochastic version of the model described in: Hennig, M., Postlethwaite, M., Forsythe, I.D. and Graham, B.P. (2007). A biophysical model of short-term plasticity at the calyx of Held. Neurocomputing, 70:1626-1629. This version introduces stochastic vesicle recycling and release. It has been used to investigate the information transmission properties of this synapse, as detailed in: Yang, Z., Hennig, M., Postlethwaite, M., Forsythe, I.D. and Graham, B.P. (2008). Wide-band information transmission at the calyx of Held. Neural Computation, 21(4):991-1018. | ||

56. | Cancelling redundant input in ELL pyramidal cells (Bol et al. 2011) | |

The paper investigates the property of the electrosensory lateral line lobe (ELL) of the brain of weakly electric fish to cancel predictable stimuli. Electroreceptors on the skin encode all signals in their firing activity, but superficial pyramidal (SP) cells in the ELL that receive this feedforward input do not respond to constant sinusoidal signals. This cancellation putatively occurs using a network of feedback delay lines and burst-induced synaptic plasticity between the delay lines and the SP cell that learns to cancel the redundant input. Biologically, the delay lines are parallel fibres from cerebellar-like granule cells in the eminentia granularis posterior. A model of this network (e.g. electroreceptors, SP cells, delay lines and burst-induced plasticity) was constructed to test whether the current knowledge of how the network operates is sufficient to cancel redundant stimuli. | ||

57. | Cell signaling/ion channel variability effects on neuronal response (Anderson, Makadia, et al. 2015) | |

" ... We evaluated the impact of molecular variability in the expression of cell signaling components and ion channels on electrophysiological excitability and neuromodulation. We employed a computational approach that integrated neuropeptide receptor-mediated signaling with electrophysiology. We simulated a population of neurons in which expression levels of a neuropeptide receptor and multiple ion channels were simultaneously varied within a physiological range. We analyzed the effects of variation on the electrophysiological response to a neuropeptide stimulus. ..." | ||

58. | Cholinergic and nicotinic regulation of DA neuron firing (Morozova et al 2020) | |

The model describes the modulation of firing properties of DA neurons by acetylcholine (ACh) and nicotine in 5 cases: knock-out of ß2-containing nAChRs, ß2-containing nAChRs only on DA neurons, the nAChRs only on GABA neurons, the nAChRs on both DA and GABA neurons and “wild” type (the AChRs on DA, GABA and Glu neurons). The distinct responses to ACh and nicotine could be explained by distinct temporal patterns of these inputs: pulsatile vs. continuous. | ||

59. | Circadian clock model based on protein sequestration (simple version) (Kim & Forger 2012) | |

"… To understand the biochemical mechanisms of this timekeeping, we have developed a detailed mathematical model of the mammalian circadian clock. Our model can accurately predict diverse experimental data including the phenotypes of mutations or knockdown of clock genes as well as the time courses and relative expression of clock transcripts and proteins. Using this model, we show how a universal motif of circadian timekeeping, where repressors tightly bind activators rather than directly binding to DNA, can generate oscillations when activators and repressors are in stoichiometric balance. …" | ||

60. | Circadian clock model in mammals (detailed version) (Kim & Forger 2012) | |

"… To understand the biochemical mechanisms of this timekeeping, we have developed a detailed mathematical model of the mammalian circadian clock. Our model can accurately predict diverse experimental data including the phenotypes of mutations or knockdown of clock genes as well as the time courses and relative expression of clock transcripts and proteins. Using this model, we show how a universal motif of circadian timekeeping, where repressors tightly bind activators rather than directly binding to DNA, can generate oscillations when activators and repressors are in stoichiometric balance. …" | ||

61. | Coding explains development of binocular vision and its failure in Amblyopia (Eckmann et al 2020) | |

This is the MATLAB code for the Active Efficient Coding model introduced in Eckmann et al 2020. It simulates an agent that self-calibrates vergence and accommodation eye movements in a simple visual environment. All algorithms are explained in detail in the main manuscript and the supplementary material of the paper. | ||

62. | Combination sensitivity and active conductances (Carlson and Kawasaki 2006) | |

"... The weakly electric fish Gymnarchus discriminates the sign of the frequency difference (Df) between a neighbor’s electric organ discharge (EOD) and its own EOD by comparing temporal patterns of amplitude modulation (AM) and phase modulation (PM). Sign-selective neurons in the midbrain respond preferentially to either positive frequency differences (Df >0 selective) or negative frequency differences (Df <0 selective). To study the mechanisms of combination sensitivity, we made whole cell intracellular recordings from sign-selective midbrain neurons in vivo and recorded postsynaptic potential (PSP) responses to AM, PM, Df >0, and Df <0. ... Responses to the nonpreferred sign of Df, but not the preferred sign of Df, were substantially weaker than linear predictions, causing a significant increase in the actual degree of sign selectivity. By using various levels of current clamp and comparing our results to simple models of synaptic integration, we demonstrate that this decreased response to the nonpreferred sign of Df is caused by a reduction in voltage-dependent excitatory conductances. This finding reveals that nonlinear decoders, in the form of voltage-dependent conductances, can enhance the selectivity of single neurons for particular combinations of stimulus attributes." See paper for more and details. | ||

63. | Comparing correlation responses to motion estimation models (Salazar-Gatzimas et al. 2016) | |

Code to generate responses of HRC-like and BL-like model elementary motion detectors to correlated noise stimuli, including two models with more realistic temporal filtering. | ||

64. | Competing oscillator 5-cell circuit and Parameterscape plotting (Gutierrez et al. 2013) | |

Our 5-cell model consists of competing fast and slow oscillators connected to a hub neuron with electrical and inhibitory synapses. Motivated by the Stomatogastric Ganglion (STG) circuit in the crab, we explored the patterns of coordination in the network as a function of the electrical coupling and inhibitory synapse strengths with the help of a novel visualization method that we call the "Parameterscape." The code submitted here will allow you to run circuit simulations and to produce a Parameterscape with the results. | ||

65. | Composite spiking network/neural field model of Parkinsons (Kerr et al 2013) | |

This code implements a composite model of Parkinson's disease (PD). The composite model consists of a leaky integrate-and-fire spiking neuronal network model being driven by output from a neural field model (instead of the more usual white noise drive). Three different sets of parameters were used for the field model: one with basal ganglia parameters based on data from healthy individuals, one based on data from individuals with PD, and one purely thalamocortical model. The aim of this model is to explore how the different dynamical patterns in each each of these field models affects the activity in the network model. | ||

66. | Computational endophenotypes in addiction (Fiore et al 2018) | |

"... here we simulated phenotypic variations in addiction symptomology and responses to putative treatments, using both a neural model, based on cortico-striatal circuit dynamics, and an algorithmic model of reinforcement learning. These simulations rely on the widely accepted assumption that both the ventral, model-based, goal-directed system and the dorsal, model-free, habitual system are vulnerable to extra-physiologic dopamine reinforcements triggered by addictive rewards. We found that endophenotypic differences in the balance between the two circuit or control systems resulted in an inverted U-shape in optimal choice behavior. Specifically, greater unbalance led to a higher likelihood of developing addiction and more severe drug-taking behaviors. ..." | ||

67. | Computational modeling of ultrasonic Subthalamic Nucleus stimulation (Tarnaud et al 2019) | |

"Objective: To explore the potential of ultrasonic modulation of plateau-potential generating subthalamic nucleus neurons (STN), by modeling their interaction with continuous and pulsed ultrasonic waves. Methods: A computational model for ultrasonic stimulation of the STN is created by combining the Otsuka-model with the bilayer sonophore model. The neuronal response to continuous and pulsed ultrasonic waves is computed in parallel for a range of frequencies, duty cycles, pulse repetition frequencies, and intensities. ..." | ||

68. | Computational modelling of channelrhodopsin-2 photocurrent characteristics (Stefanescu et al. 2013) | |

The codes are directly related with the results presented in the manuscript; in brief, it is a computational investigation on the effects of optogenetic actuation on excitatory and inhibitory neurons when 3- and 4- state model is used to implement the ChR2 kinetics. Different parameters of optostimulation are investigated and the results compared with experimental data previously published by other research groups. | ||

69. | CONFIGR: a vision-based model for long-range figure completion (Carpenter et al. 2007) | |

"CONFIGR (CONtour FIgure GRound) is a computational model based on principles of biological vision that completes sparse and noisy image figures. Within an integrated vision/recognition system, CONFIGR posits an initial recognition stage which identifies figure pixels from spatially local input information. The resulting, and typically incomplete, figure is fed back to the “early vision” stage for long-range completion via filling-in. The reconstructed image is then re-presented to the recognition system for global functions such as object recognition. ... Multi-scale simulations illustrate the vision/recognition system. ..." | ||

70. | Continuous time stochastic model for neurite branching (van Elburg 2011) | |

"In this paper we introduce a continuous time stochastic neurite branching model closely related to the discrete time stochastic BES-model. The discrete time BES-model is underlying current attempts to simulate cortical development, but is difficult to analyze. The new continuous time formulation facilitates analytical treatment thus allowing us to examine the structure of the model more closely. ..." | ||

71. | Continuum model of tubulin-driven neurite elongation (Graham et al 2006) | |

This model investigates the elongation over time of a single developing neurite (axon or dendrite). Our neurite growth model describes the elongation of a single,unbranched neurite in terms of the rate of extension of the microtubule cytoskeleton. The cytoskeleton is not explicitly modelled, but its construction is assumed to depend on the available free tubulin at the growing neurite tip. | ||

72. | Convergence regulates synchronization-dependent AP transfer in feedforward NNs (Sailamul et al 2017) | |

We study how synchronization-dependent spike transfer can be affected by the structure of convergent feedforward wiring. We implemented computer simulations of model neural networks: a source and a target layer connected with different types of convergent wiring rules. In the Gaussian-Gaussian (GG) model, both the connection probability and the strength are given as Gaussian distribution as a function of spatial distance. In the Uniform-Constant (UC) and Uniform-Exponential (UE) models, the connection probability density is a uniform constant within a certain range, but the connection strength is set as a constant value or an exponentially decaying function, respectively. Then we examined how the spike transfer function is modulated under these conditions, while static or synchronized input patterns were introduced to simulate different levels of feedforward spike synchronization. We observed that the synchronization-dependent modulation of the transfer function appeared noticeably different for each convergence condition. The modulation of the spike transfer function was largest in the UC model, and smallest in the UE model. Our analysis showed that this difference was induced by the different spike weight distributions that was generated from convergent synapses in each model. Our results suggest that the structure of the feedforward convergence is a crucial factor for correlation-dependent spike control, thus must be considered important to understand the mechanism of information transfer in the brain. | ||

73. | Core respiratory network organization: Insights from optogenetics and modeling (Ausborn et al 2018) | |

"The circuit organization within the mammalian brainstem respiratory network, specifically within and between the pre-Bötzinger (pre-BötC) and Bötzinger (BötC) complexes, and the roles of these circuits in respiratory pattern generation are continuously debated. We address these issues with a combination of optogenetic experiments and modeling studies. We used transgenic mice expressing channelrhodopsin-2 under the VGAT-promoter to investigate perturbations of respiratory circuit activity by site-specific photostimulation of inhibitory neurons within the pre-BötC or BötC. The stimulation effects were dependent on the intensity and phase of the photostimulation. Specifically: (1) Low intensity (= 1.0 mW) pulses delivered to the pre-BötC during inspiration did not terminate activity, whereas stronger stimulations (= 2.0 mW) terminated inspiration. (2) When the pre-BötC stimulation ended in or was applied during expiration, rebound activation of inspiration occurred after a fixed latency. (3) Relatively weak sustained stimulation (20 Hz, 0.5–2.0 mW) of pre-BötC inhibitory neurons increased respiratory frequency, while a further increase of stimulus intensity (> 3.0 mW) reduced frequency and finally (= 5.0 mW) terminated respiratory oscillations. (4) Single pulses (0.2–5.0 s) applied to the BötC inhibited rhythmic activity for the duration of the stimulation. (5) Sustained stimulation (20 Hz, 0.5–3.0 mW) of the BötC reduced respiratory frequency and finally led to apnea. We have revised our computational model of pre-BötC and BötC microcircuits by incorporating an additional population of post-inspiratory inhibitory neurons in the pre-BötC that interacts with other neurons in the network. This model was able to reproduce the above experimental findings as well as previously published results of optogenetic activation of pre-BötC or BötC neurons obtained by other laboratories. The proposed organization of pre-BötC and BötC circuits leads to testable predictions about their specific roles in respiratory pattern generation and provides important insights into key circuit interactions operating within brainstem respiratory networks." | ||

74. | Cortex-Basal Ganglia-Thalamus network model (Kumaravelu et al. 2016) | |

" ... We developed a biophysical network model comprising of the closed loop cortical-basal ganglia-thalamus circuit representing the healthy and parkinsonian rat brain. The network properties of the model were validated by comparing responses evoked in basal ganglia (BG) nuclei by cortical (CTX) stimulation to published experimental results. A key emergent property of the model was generation of low-frequency network oscillations. Consistent with their putative pathological role, low-frequency oscillations in model BG neurons were exaggerated in the parkinsonian state compared to the healthy condition. ..." | ||

75. | Cortico - Basal Ganglia Loop (Mulcahy et al 2020) | |

The model represents learning and reversal tasks and shows performance in control, Parkinsonian and Huntington disease conditions | ||

76. | Cortico-striatal plasticity in medium spiny neurons (Gurney et al 2015) | |

In the associated paper (Gurney et al, PLoS Biology, 2015) we presented a computational framework that addresses several issues in cortico-striatal plasticity including spike timing, reward timing, dopamine level, and dopamine receptor type. Thus, we derived a complete model of dopamine and spike-timing dependent cortico-striatal plasticity from in vitro data. We then showed this model produces the predicted activity changes necessary for learning and extinction in an operant task. Moreover, we showed the complex dependencies of cortico-striatal plasticity are not only sufficient but necessary for learning and extinction. The model was validated in a wider setting of action selection in basal ganglia, showing how it could account for behavioural data describing extinction, renewal, and reacquisition, and replicate in vitro experimental data on cortico-striatal plasticity. The code supplied here allows reproduction of the proposed process of learning in medium spiny neurons, giving the results of Figure 7 of the paper. | ||

77. | Decoding movement trajectory from simulated grid cell population activity (Bush & Burgess 2019) | |

Matlab code to simulate a population of grid cells that exhibit both a rate and phase code for location in 1D or 2D environments, and are modulated by a human hippocampal LFP signal with highly variable frequency; then subsequently decode location, running speed, movement direction and an arbitrary fourth variable from population firing rates and phases in each oscillatory cycle. | ||

78. | Deep belief network learns context dependent behavior (Raudies, Zilli, Hasselmo 2014) | |

We tested a rule generalization capability with a Deep Belief Network (DBN), Multi-Layer Perceptron network, and the combination of a DBN with a linear perceptron (LP). Overall, the combination of the DBN and LP had the highest success rate for generalization. | ||

79. | Dendritic Na inactivation drives a decrease in ISI (Fernandez et al 2005) | |

We use a combination of dynamical analysis and electrophysiological recordings to demonstrate that spike broadening in dendrites is primarily caused by a cumulative inactivation of dendritic Na(+) current. We further show that a reduction in dendritic Na(+) current increases excitability by decreasing the interspike interval (ISI) and promoting burst firing. | ||

80. | Dendritic properties control energy efficiency of APs in cortical pyramidal cells (Yi et al 2017) | |

Neural computation is performed by transforming input signals into sequences of action potentials (APs), which is metabolically expensive and limited by the energy available to the brain. The energy efficiency of single AP has important consequences for the computational power of the cell, which is determined by its biophysical properties and morphologies. Here we adopt biophysically-based two-compartment models to investigate how dendrites affect energy efficiency of APs in cortical pyramidal neurons. We measure the Na+ entry during the spike and examine how it is efficiently used for generating AP depolarization. We show that increasing the proportion of dendritic area or coupling conductance between two chambers decreases Na+ entry efficiency of somatic AP. Activating inward Ca2+ current in dendrites results in dendritic spike, which increases AP efficiency. Activating Ca2+-activated outward K+ current in dendrites, however, decreases Na+ entry efficiency. We demonstrate that the active and passive dendrites take effects by altering the overlap between Na+ influx and internal current flowing from soma to dendrite. We explain a fundamental link between dendritic properties and AP efficiency, which is essential to interpret how neural computation consumes metabolic energy and how the biophysics and morphology contributes to such consumption. | ||

81. | Detailed analysis of trajectories in the Morris water maze (Gehring et al. 2015) | |

MATLAB code that can be used for detailed behavioural analyzes of the trajectories of animals be means of a semi-supervised clustering algorithm. The method is applied here to trajectories in the Morris Water Maze (see Gehring, T. V. et al., Scientific Reports, 2015) but the code can easily be adapted to other types experiments. For more information and the latest version of the code please refer to https://bitbucket.org/tiagogehring/mwm_trajectories | ||

82. | Development and Binocular Matching of Orientation Selectivity in Visual Cortex (Xu et al 2020) | |

This model investigates the development of orientation selectivity and its binocular matching in visual cortex by implementing a neuron that has plastic synapses for its inputs from the left and right eye. The plasticity is taken to be voltage-based with homeostasis (Clopath et al 2010). The neuron is modeled as an adaptive exponential integrate-fire neuron. The uploaded model has been used in Xu, Cang & Riecke (2020) to analyze the impact of ocular dominance and orientation selectivity on the matching process. There it has been found that the matching can proceed by a slow shifting or a sudden switching of the preferred orientation. | ||

83. | Dipole Localization Kit (Mechler & Victor, 2012) | |

We localize a single neuron from the spatial sample of its EAP amplitudes recorded with a multisite probe (with 6 or more independent measurement sites or channels, e.g., a silicon polytrode, a stepped tetrode, etc.) This is an inverse problem and we solve it by fitting a model to the EAPs that consists of a volume conductor model of the neural tissue (known), a realistic model of the probe (known), and a single dipole current source of the model neuron (unknown). The dipole is free to change position, size, and orientation (a total of 6 parameters) at each moment during the action potential. | ||

84. | Direct recruitment of S1 pyramidal cells and interneurons via ICMS (Overstreet et al., 2013) | |

Study of the pyramidal cells and interneurons recruited by intracortical microstimulation in primary somatosensory cortex. Code includes morphological models for seven types of pyramidal cells and eight types of interneurons, NEURON code to simulate ICMS, and an artificial reconstruction of a 3D slab of cortex implemented in MATLAB. | ||

85. | Disentangling astroglial physiology with a realistic cell model in silico (Savtchenko et al 2018) | |

"Electrically non-excitable astroglia take up neurotransmitters, buffer extracellular K+ and generate Ca2+ signals that release molecular regulators of neural circuitry. The underlying machinery remains enigmatic, mainly because the sponge-like astrocyte morphology has been difficult to access experimentally or explore theoretically. Here, we systematically incorporate multi-scale, tri-dimensional astroglial architecture into a realistic multi-compartmental cell model, which we constrain by empirical tests and integrate into the NEURON computational biophysical environment. This approach is implemented as a flexible astrocyte-model builder ASTRO. As a proof-of-concept, we explore an in silico astrocyte to evaluate basic cell physiology features inaccessible experimentally. ..." | ||

86. | Distributed cerebellar plasticity implements adaptable gain control (Garrido et al., 2013) | |

We tested the role of plasticity distributed over multiple synaptic sites (Hansel et al., 2001; Gao et al., 2012) by generating an analog cerebellar model embedded into a control loop connected to a robotic simulator. The robot used a three-joint arm and performed repetitive fast manipulations with different masses along an 8-shape trajectory. In accordance with biological evidence, the cerebellum model was endowed with both LTD and LTP at the PF-PC, MF-DCN and PC-DCN synapses. This resulted in a network scheme whose effectiveness was extended considerably compared to one including just PF-PC synaptic plasticity. Indeed, the system including distributed plasticity reliably self-adapted to manipulate different masses and to learn the arm-object dynamics over a time course that included fast learning and consolidation, along the lines of what has been observed in behavioral tests. In particular, PF-PC plasticity operated as a time correlator between the actual input state and the system error, while MF-DCN and PC-DCN plasticity played a key role in generating the gain controller. This model suggests that distributed synaptic plasticity allows generation of the complex learning properties of the cerebellum. | ||

87. | Distributed representation of perceptual categories in the auditory cortex (Kim and Bao 2008) | |

Examines the hypothesis that enlargement in cortical stimulus representation is a mechanism of categorical perception. Categorical perception is tested using discrimination and identification ability. | ||

88. | Distributed synaptic plasticity and spike timing (Garrido et al. 2013) | |

Here we have used a computational model to simulate the impact of multiple distributed synaptic weights in the cerebellar granular layer network. In response to mossy fiber bursts, synaptic weights at multiple connections played a crucial role to regulate spike number and positioning in granule cells. Interestingly, different combinations of synaptic weights optimized either first-spike timing precision or spike number, efficiently controlling transmission and filtering properties. These results predict that distributed synaptic plasticity regulates the emission of quasi-digital spike patterns on the millisecond time scale and allows the cerebellar granular layer to flexibly control burst transmission along the mossy fiber pathway. | ||

89. | Dopamine-modulated medium spiny neuron, reduced model (Humphries et al. 2009) | |

We extended Izhikevich's reduced model of the striatal medium spiny neuron (MSN) to account for dopaminergic modulation of its intrinsic ion channels and synaptic inputs. We tuned our D1 and D2 receptor MSN models using data from a recent (Moyer et al, 2007) large-scale compartmental model. Our new models capture the input-output relationships for both current injection and spiking input with remarkable accuracy, despite the order of magnitude decrease in system size. They also capture the paired pulse facilitation shown by MSNs. Our dopamine models predict that synaptic effects dominate intrinsic effects for all levels of D1 and D2 receptor activation. Our analytical work on these models predicts that the MSN is never bistable. Nonetheless, these MSN models can produce a spontaneously bimodal membrane potential similar to that recently observed in vitro following application of NMDA agonists. We demonstrate that this bimodality is created by modelling the agonist effects as slow, irregular and massive jumps in NMDA conductance and, rather than a form of bistability, is due to the voltage-dependent blockade of NMDA receptors | ||

90. | Drosophila 3rd instar larval aCC motoneuron (Gunay et al. 2015) | |

Single compartmental, ball-and-stick models implemented in XPP and full morphological model in Neuron. Paper has been submitted and correlates anatomical properties with electrophysiological recordings from these hard-to-access neurons. For instance we make predictions about location of the spike initiation zone, channel distributions, and synaptic input parameters. | ||

91. | Drosophila circadian clock neurone model of essential tremor (Smith et al 2018) | |

Model of Drosophila ventral lateral neuron (LNV) used to study a human ion channel associated with essential tremor. | ||

92. | Drosophila lateral ventral clock neuron (LNV) model (Smith et al 2019) | |

LNVmodel models the activity of a Drosophila lateral ventral clock neurons (LNV) neurone. | ||

93. | Effect of circuit structure on odor representation in insect olfaction (Rajagopalan & Assisi 2020) | |

"How does the structure of a network affect its function? We address this question in the context of two olfactory systems that serve the same function, to distinguish the attributes of different odorants, but do so using markedly distinct architectures. In the locust, the probability of connections between projection neurons and Kenyon cells - a layer downstream - is nearly 50%. In contrast, this number is merely 5% in drosophila. We developed computational models of these networks to understand the relative advantages of each connectivity. Our analysis reveals that the two systems exist along a continuum of possibilities that balance two conflicting goals – separating the representations of similar odors while grouping together noisy variants of the same odor." | ||

94. | Effect of cortical D1 receptor sensitivity on working memory maintenance (Reneaux & Gupta 2018) | |

Alterations in cortical D1 receptor density and reactivity of dopamine-binding sites, collectively termed as D1 receptor-sensitivity in the present study, have been experimentally shown to affect the working memory maintenance during delay-period. However, computational models addressing the effect of D1 receptor-sensitivity are lacking. A quantitative neural mass model of the prefronto-mesoprefrontal system has been proposed to take into account the effect of variation in cortical D1 receptor-sensitivity on working memory maintenance during delay. The model computes the delay-associated equilibrium states/operational points of the system for different values of D1 receptor-sensitivity through the nullcline and bifurcation analysis. Further, to access the robustness of the working memory maintenance during delay in the presence of alteration in D1 receptor-sensitivity, numerical simulations of the stochastic formulation of the model are performed to obtain the global potential landscape of the dynamics. | ||

95. | Effect of ionic diffusion on extracellular potentials (Halnes et al 2016) | |

"Recorded potentials in the extracellular space (ECS) of the brain is a standard measure of population activity in neural tissue. Computational models that simulate the relationship between the ECS potential and its underlying neurophysiological processes are commonly used in the interpretation of such measurements. Standard methods, such as volume-conductor theory and current-source density theory, assume that diffusion has a negligible effect on the ECS potential, at least in the range of frequencies picked up by most recording systems. This assumption remains to be verified. We here present a hybrid simulation framework that accounts for diffusive effects on the ECS potential. ..." | ||

96. | Effects of the membrane AHP on the Lateral Superior Olive (LSO) (Zhou & Colburn 2010) | |

This simulation study investigated how membrane afterhyperpolarization (AHP) influences spiking activity of neurons in the Lateral Superior Olive (LSO). The model incorporates a general integrate-and-fire spiking mechanism with a first-order adaptation channel. Simulations focus on differentiating the effects of GAHP, tauAHP, and input strength on (1) spike interval statistics, such as negative serial correlation and chopper onset, and (2) neural sensitivity to interaural level difference (ILD) of LSO neurons. The model simulated electrophysiological data collected in cat LSO (Tsuchitani and Johnson, 1985). | ||

97. | Efficient estimation of detailed single-neuron models (Huys et al. 2006) | |

"Biophysically accurate multicompartmental models of individual neurons ... depend on a large number of parameters that are difficult to estimate. ... We propose a statistical approach to the automatic estimation of various biologically relevant parameters, including 1) the distribution of channel densities, 2) the spatiotemporal pattern of synaptic input, and 3) axial resistances across extended dendrites. ... We demonstrate that the method leads to accurate estimations on a wide variety of challenging model data sets that include up to about 10,000 parameters (roughly two orders of magnitude more than previously feasible) and describe how the method gives insights into the functional interaction of groups of channels." | ||

98. | Electrical activity of the suprachiasmatic nuclei (Stinchcombe et al. 2017) | |

A network of SCN neurons coupled though GABA synapses with a light input current. | ||

99. | Electrodiffusive astrocytic and extracellular ion concentration dynamics model (Halnes et al. 2013) | |

An electrodiffusive formalism was developed for computing the dynamics of the membrane potential and ion concentrations in the intra- and extracellular space in a one-dimensional geometry (cable). This (general) formalism was implemented in a model of astrocytes exchanging K+, Na+ and Cl- ions with the extracellular space (ECS). A limited region (0< x<l/10 where l is the astrocyte length) of the ECS was exposed to an increase in the local K+ concentration. The model is used to explore how astrocytes contribute in transporting K+ out from high-concentration regions via a mechanism known as spatial buffering, which involves local uptake from high concentration regions, intracellular transport, and release of K+ in regions with lower ECS concentrations. | ||

100. | Elementary mechanisms producing facilitation of Cav2.1 (P/Q-type) channels | |

"The regulation of Ca(V)2.1 (P/Q-type) channels by calmodulin (CaM) showcases the powerful Ca(2+) decoding capabilities of CaM in complex with the family of Ca(V)1-2 Ca(2+) channels. Throughout this family, CaM does not simply exert a binary on/off regulatory effect; rather, Ca(2+) binding to either the C- or N-terminal lobe of CaM alone can selectively trigger a distinct form of channel modulation. ... Ca(2+) binding to the C-terminal lobe induces Ca(2+)-dependent facilitation of opening (CDF), whereas the N-terminal lobe yields Ca(2+)-dependent inactivation of opening (CDI). ... Furthermore, direct single-channel determinations of channel open probability (P(o)) and kinetic simulations demonstrate that CDF represents a genuine enhancement of open probability, without appreciable change of activation kinetics. This enhanced-opening mechanism suggests that the CDF evoked during action-potential trains would produce not only larger, but longer-lasting Ca(2+) responses, an outcome with potential ramifications for short-term synaptic plasticity." | ||

101. | ELL pyramidal neuron (Simmonds and Chacron 2014) | |

network model of ELL pyramidal neurons receiving both feedforward and feedback inputs | ||

102. | Emergence of Connectivity Motifs in Networks of Model Neurons (Vasilaki, Giugliano 2014) | |

Recent evidence suggests that short-term dynamics of excitatory synaptic transmission is correlated to stereotypical connectivity motifs. We show that these connectivity motifs emerge in networks of model neurons, from the interactions between short-term synaptic dynamics (SD) and long-term spike-timing dependent plasticity (STDP). | ||

103. | Entrainment and divisive inhibition in a neocortical neural mass model (Papasavvas et al 2020) | |

Neural mass model of a neocortical microcircuit featuring one excitatory and two inhibitory populations. The inhibitory populations represent the soma-targeting (parvalbumin) and dendrite-targeting (somatostatin) interneurons. The model uses the Wilson-Cowan formalism and differentiates between the two inhibitory populations by the way they modulate the input-output function of the excitatory population (subtractive vs divisive inhibition, based on Wilson et al., Nature, 7411, 488, 343-348, 2012). The connectivity patterns between the populations follow the patterns reported in the primary visual cortex (Pfeffer et al., Nat Neurosci 16, 1068–1076, 2013). The model is used here to investigate the role of divisive inhibition during the entrainment of the microcircuit. | ||

104. | Ephaptic coupling in passive cable and MSO neuron models (Goldwyn & Rinzel 2016) | |

Simulation code to explore how the synchronous activity of a bundle of neurons generates extracellular voltage, and how this extracellular voltage influences the membrane potential of "nearby" neurons. A non-synaptic mechanism known as ephaptic coupling. A model of a passive cable population (including user-friendly matlab GUI) and a model of medial superior olive neurons are included. | ||

105. | Epileptic seizure model with Morris-Lecar neurons (Beverlin and Netoff 2011) | |

Here we use phase-response curves (PRC) from Morris-Lecar (M-L) model neurons with synaptic depression and gradually decrease input current to cells within a network simulation. This method effectively decreases firing rates resulting in a shift to greater network synchrony illustrating a possible mechanism of the transition phenomenon. PRCs are measured from the M-L conductance based model cell with a range of input currents within the limit cycle. A large network of 3000 excitatory neurons is simulated with a network topology generated from second-order statistics which allows a range of population synchrony. The population synchrony of the oscillating cells is measured with the Kuramoto order parameter, which reveals a transition from tonic to clonic phase exhibited by our model network. | ||

106. | Estimation of conductance in a conductance-based model of quadratic type (Vich & Guillamon 2015) | |

We assume to have a quadratic approximation of a conductance-based neuron model, as in H.Rotstein (2015). Given the resulting membrane potential (v) and the course of the gating variable (w), this program estimates the synaptic current that the neuron is receiving at each time. Moreover, given the voltage traces for two different applied (steady) currents and the excitatory and inhibitory reversal potentials, the program estimates the excitatory and inhibitory conductances separately. Finally, the program gives the option of estimating the synaptic conductance. This conductance can be estimated in two different ways: (1) if only one voltage trace is given, the synaptic conductance is estimated using the synaptic reversal potential; (2) however, if two voltage traces are given (for two different applied currents), then the synaptic conductance can be either estimated using the synaptic reversal potential or the leak conductance. | ||

107. | Excessive beta oscillations in Parkinson's disease (Pavlides et al. 2015) | |

" ... Understanding the generation of beta oscillations is important to improve treatments for Parkinson’s disease. Competing theories exist for how these oscillations are generated in the affected brain circuits, which include the motor cortex and a set of subcortical nuclei called the basal ganglia. This paper suggests two hypotheses for the generation of beta oscillations. The first hypothesis is that beta oscillations are generated in the motor cortex, and the basal ganglia resonate to the cortical input. The second hypothesis additionally proposes that feedback from the basal ganglia to cortex is critically important for the presence of the oscillations. We show that the models can successfully account for a wide range of experimental data concerning the presence of beta oscillations in Parkinson’s disease." | ||

108. | Excitability of DA neurons and their regulation by synaptic input (Morozova et al. 2016a, 2016b) | |

This code contains conductance-based models of Dopaminergic (DA) and GABAergic neurons, used in Morozova et al 2016 PLOS Computational Biology paper in order to study the type of excitability of the DA neurons and how it is influenced by the intrinsic and synaptic currents. We identified the type of excitability by calculating bifurcation diagrams and F-I curves using XPP file. This model was also used in Morozova et al 2016 J. Neurophysiology paper in order to study the effect of synchronization in GABAergic inputs on the firing dynamics of the DA neuron. | ||

109. | Excitation Properties of Computational Models of Unmyelinated Peripheral Axons (Pelot et al., 2020) | |

We implemented the single-compartment model of vagal afferents from Schild et al. 1994 and extended the model into a multi-compartment axon, presenting the first C-fiber cable model of a C-fiber vagal afferent. We also implemented the updated parameters from Schild and Kunze 1997. We compared the responses of these novel models to three published models of unmyelinated axons (Rattay and Aberham 1993; Sundt et al. 2015; Tigerholm et al. 2014). | ||

110. | Excitation-contraction coupling in an integrative heart cell model (Greenstein et al 2006) | |

"... In this study, we generalize a recently developed analytical approach for deriving simplified mechanistic models of CICR (Ca(2+)-induced Ca(2+) release) to formulate an integrative model of the canine cardiac myocyte which is computationally efficient. The resulting model faithfully reproduces experimentally measured properties of EC (excitation-contraction) coupling and whole cell phenomena. The model is used to study the role of local redundancy in L-type Ca(2+) channel gating and the role of dyad configuration on EC coupling. Simulations suggest that the characteristic steep rise in EC coupling gain observed at hyperpolarized potentials is a result of increased functional coupling between LCCs (L-type Ca(2+) channels) and RyRs (ryanodine-sensitive Ca(2+) release channels). We also demonstrate mechanisms by which alterations in the early repolarization phase of the action potential, resulting from reduction of the transient outward potassium current, alters properties of EC coupling." | ||

111. | Excitotoxic loss of dopaminergic cells in PD (Muddapu et al 2019) | |

"... A couple of the proposed mechanisms, however, show potential for the development of a novel line of PD (Parkinson's disease) therapeutics. One of these mechanisms is the peculiar metabolic vulnerability of SNc (Substantia Nigra pars compacta) cells compared to other dopaminergic clusters; the other is the SubThalamic Nucleus (STN)-induced excitotoxicity in SNc. To investigate the latter hypothesis computationally, we developed a spiking neuron network-model of SNc-STN-GPe system. In the model, prolonged stimulation of SNc cells by an overactive STN leads to an increase in ‘stress’ variable; when the stress in a SNc neuron exceeds a stress threshold, the neuron dies. The model shows that the interaction between SNc and STN involves a positive-feedback due to which, an initial loss of SNc cells that crosses a threshold causes a runaway-effect, leading to an inexorable loss of SNc cells, strongly resembling the process of neurodegeneration. The model further suggests a link between the two aforementioned mechanisms of SNc cell loss. Our simulation results show that the excitotoxic cause of SNc cell loss might initiate by weak-excitotoxicity mediated by energy deficit, followed by strong-excitotoxicity, mediated by a disinhibited STN. A variety of conventional therapies were simulated to test their efficacy in slowing down SNc cell loss. Among them, glutamate inhibition, dopamine restoration, subthalamotomy and deep brain stimulation showed superior neuroprotective-effects in the proposed model." | ||

112. | External Tufted Cell Model (Ryan Viertel, Alla Borisyuk 2019) | |

ODE model of the Mammalian External Tufted Cell | ||

113. | Extracellular Action Potential Simulations (Gold et al 2007) | |

This package recreates the the principal experiments described in (Gold, Henze and Koch, 2007) and includes the core code necessary to create your own Extracellular Action Potential Simulations. | ||

114. | Extraction and classification of three cortical neuron types (Mensi et al. 2012) | |

This script proposes a new convex fitting procedure that allows the parameters estimation of a large class of stochastic Integrate-and-Fire model upgraded with spike-triggered current and moving threshold from patch-clamp experiments (i.e. given the injected current and the recorded membrane potential). This script applies the method described in the paper to estimate the parameters of a reference model from a single voltage trace and the corresponding input current and evaluate the performance of the fitted model on a separated test set. | ||

115. | Failure of Deep Brain Stimulation in a basal ganglia neuronal network model (Dovzhenok et al. 2013) | |

"… Recently, a lot of interest has been devoted to desynchronizing delayed feedback deep brain stimulation (DBS). ... This study explores the action of delayed feedback stimulation on partially synchronized oscillatory dynamics, similar to what one observes experimentally in parkinsonian patients. …" Implemented by Andrey Dovzhenok, to whom questions should be addressed. | ||

116. | Fast population coding (Huys et al. 2007) | |

"Uncertainty coming from the noise in its neurons and the ill-posed nature of many tasks plagues neural computations. Maybe surprisingly, many studies show that the brain manipulates these forms of uncertainty in a probabilistically consistent and normative manner, and there is now a rich theoretical literature on the capabilities of populations of neurons to implement computations in the face of uncertainty. However, one major facet of uncertainty has received comparatively little attention: time. In a dynamic, rapidly changing world, data are only temporarily relevant. Here, we analyze the computational consequences of encoding stimulus trajectories in populations of neurons. ..." | ||

117. | Fast Spiking Basket cells (Tzilivaki et al 2019) | |

"Interneurons are critical for the proper functioning of neural circuits. While often morphologically complex, dendritic integration and its role in neuronal output have been ignored for decades, treating interneurons as linear point neurons. Exciting new findings suggest that interneuron dendrites support complex, nonlinear computations: sublinear integration of EPSPs in the cerebellum, coupled to supralinear calcium accumulations and supralinear voltage integration in the hippocampus. These findings challenge the point neuron dogma and call for a new theory of interneuron arithmetic. Using detailed, biophysically constrained models, we predict that dendrites of FS basket cells in both hippocampus and mPFC come in two flavors: supralinear, supporting local sodium spikes within large-volume branches and sublinear, in small-volume branches. Synaptic activation of varying sets of these dendrites leads to somatic firing variability that cannot be explained by the point neuron reduction. Instead, a 2-stage Artificial Neural Network (ANN), with both sub- and supralinear hidden nodes, captures most of the variance. We propose that FS basket cells have substantially expanded computational capabilities sub-served by their non-linear dendrites and act as a 2-layer ANN." | ||

118. | Feature integration drives probabilistic behavior in Fly escape response (von Reyn et al 2017) | |

"... A Linear Model for Visual Feature Integration in the GF (Drosophila Giant Fiber) Circuit. To test our hypothesis that the GFs linearly integrate the separately encoded features of looming stimulus size and angular velocity, we developed a model to predict GF membrane potential across visual stimuli (Figure 8A). In this four-component model, the GFs linearly sum two excitatory components— non-LC4(Type 4 lobula columnar neurons)-mediated angular size excitation and LC4-mediated angular velocity excitation—and two inhibitory components— non-LC4- and LC4-mediated angular size inhibition." | ||

119. | FFV1MT: A V1-MT feedforward architecture for optical flow estimation (Solari et a., 2015) | |

A neural feed-forward model composed of two layers that mimic the V1-MT primary motion pathway, derived from previous works by Heeger and Simoncelli. | ||

120. | Fisher and Shannon information in finite neural populations (Yarrow et al. 2012) | |

Here we model populations of rate-coding neurons with bell-shaped tuning curves and multiplicative Gaussian noise. This Matlab code supports the calculation of information theoretic (mutual information, stimulus-specific information, stimulus-specific surprise) and Fisher-based measures (Fisher information, I_Fisher, SSI_Fisher) in these population models. The information theoretic measures are computed by Monte Carlo integration, which allows computationally-intensive decompositions of the mutual information to be computed for relatively large populations (hundreds of neurons). | ||

121. | Fitting predictive coding to the neurophysiological data (Spratling 2019) | |

MATLAB code for simulating the response properties of V1 mismatch neurons and for testing the ability of predictive coding algorithms to scale. This code performs the experiments described in: Spratling MW (2019) Abstract: "Recent neurophysiological data showing the effects of locomotion on neural activity in mouse primary visual cortex has been interpreted as providing strong support for the predictive coding account of cortical function. Specifically, this work has been interpreted as providing direct evidence that prediction-error, a distinguishing property of predictive coding, is encoded in cortex. This article evaluates these claims and highlights some of the discrepancies between the proposed predictive coding model and the neuro-biology. Furthermore, it is shown that the model can be modified so as to fit the empirical data more successfully." | ||

122. | Fractional leaky integrate-and-fire model (Teka et al. 2014) | |

We developed the Fractional Leaky Integrate-and-Fire model that can produce downward and upward spike time adaptions observed on pyramidal cells.The adaptation emerges from the fractional exponent of the voltage dynamics. | ||

123. | FRAT: An amygdala-centered model of fear conditioning (Krasne et al. 2011) | |

Model of Pavlovian fear conditioning and extinction (due to neuromodulator-controlled LTP on principal cells and inhibory interneurons)occur in amygdala and contextual representations are learned in hippocampus. Many properties of fear conditioning are accounted for. | ||

124. | Function and energy constrain neuronal biophysics in coincidence detection (Remme et al 2018) | |

" ... We use models of conductance-based neurons constrained by experimentally observed characteristics with parameters varied within a physiologically realistic range. Our study shows that neuronal design of MSO cells does not compromise on function, but favors energetically less costly cell properties where possible without interfering with function." | ||

125. | Gap junction coupled network of striatal fast spiking interneurons (Hjorth et al. 2009) | |

Gap junctions between striatal FS neurons has very weak ability to synchronise spiking. Input uncorrelated between neighbouring neurons is shunted, while correlated input is not. | ||

126. | Generation of granule cell dendritic morphology (Schneider et al. 2014) | |

The following code was used to generate a complete population of 1.2 million granule cell dendritic morphologies within a realistic three-dimensional context. These generated dendritic morphologies match the known biological variability and context-dependence of morphological features. | ||

127. | Generation of stable heading representations in diverse visual scenes (Kim et al 2019) | |

"Many animals rely on an internal heading representation when navigating in varied environments. How this representation is linked to the sensory cues that define different surroundings is unclear. In the fly brain, heading is represented by ‘compass’ neurons that innervate a ring-shaped structure known as the ellipsoid body. Each compass neuron receives inputs from ‘ring’ neurons that are selective for particular visual features; this combination provides an ideal substrate for the extraction of directional information from a visual scene. Here we combine two-photon calcium imaging and optogenetics in tethered flying flies with circuit modelling, and show how the correlated activity of compass and visual neurons drives plasticity, which flexibly transforms two-dimensional visual cues into a stable heading representation. ... " See the supplementary information for model details. | ||

128. | Generic Bi-directional Real-time Neural Interface (Zrenner et al. 2010) | |

Matlab/Simulink toolkit for generic multi-channel short-latency bi-directional neural-computer interactions. High-bandwidth (> 10 megabit per second) neural recording data can be analyzed in real-time while simultaneously generating specific complex electrical stimulation feedback with deterministically timed responses at sub-millisecond resolution. The commercially available 60-channel extracellular multi-electrode recording and stimulation set-up (Multichannelsystems GmbH MEA60) is used as an example hardware implementation. | ||

129. | Graph-theoretical Derivation of Brain Structural Connectivity (Giacopelli et al 2020) | |

Brain connectivity at the single neuron level can provide fundamental insights into how information is integrated and propagated within and between brain regions. However, it is almost impossible to adequately study this problem experimentally and, despite intense efforts in the field, no mathematical description has been obtained so far. Here, we present a mathematical framework based on a graph-theoretical approach that, starting from experimental data obtained from a few small subsets of neurons, can quantitatively explain and predict the corresponding full network properties. This model also changes the paradigm with which large-scale model networks can be built, from using probabilistic/empiric connections or limited data, to a process that can algorithmically generate neuronal networks connected as in the real system. | ||

130. | Grid cell model with compression effects (Raudies & Hasselmo, 2015) | |

We present a model for compression of grid cell firing in modules to changes in barrier location. | ||

131. | Grid cell oscillatory interference with noisy network oscillators (Zilli and Hasselmo 2010) | |

To examine whether an oscillatory interference model of grid cell activity could work if the oscillators were noisy neurons, we implemented these simulations. Here the oscillators are networks (either synaptically- or gap-junction--coupled) of one or more noisy neurons (either Izhikevich's simple model or a Hodgkin-Huxley--type biophysical model) which drive a postsynaptic cell (which may be integrate-and-fire, resonate-and-fire, or the simple model) which should fire spatially as a grid cell if the simulation is successful. | ||

132. | Grid cell spatial firing models (Zilli 2012) | |

This package contains MATLAB implementations of most models (published from 2005 to 2011) of the hexagonal firing field arrangement of grid cells. | ||

133. | Grid cells from place cells (Castro & Aguiar, 2014) | |

" ...Here we present a novel model for the emergence of gridlike firing patterns that stands on two key hypotheses: (1) spatial information in GCs is provided from PC activity and (2) grid fields result from a combined synaptic plasticity mechanism involving inhibitory and excitatory neurons mediating the connections between PCs and GCs. ..." | ||

134. | Hebbian learning in a random network for PFC modeling (Lindsay, et al. 2017) | |

Creates a random model that replicates the inputs and outputs of PFC cells during a complex task. Then executes Hebbian learning in the model and performs a set of analyses on the output. A portion of this model's analysis requires code from: https://github.com/brian-lau/highdim | ||

135. | Hebbian STDP for modelling the emergence of disparity selectivity (Chauhan et al 2018) | |

This code shows how Hebbian learning mediated by STDP mechanisms could explain the emergence of disparity selectivity in the early visual system. This upload is a snapshot of the code at the time of acceptance of the paper. For a link to a soon-to-come git repository, consult the author's website: www.tusharchauhan.com/research/ . The datasets used in the paper are not provided due to size, but download links and expected directory-structures are. The user can (and is strongly encouraged to) experiment with their own dataset. Let me know if you find something interesting! Finally, I am very keen on a redesign/restructure/adaptation of the code to more applied problems in AI and robotics (or any other field where a spiking non-linear approach makes sense). If you have a serious proposal, don't hesitate to contact me [research AT tusharchauhan DOT com ]. | ||

136. | HH model neuron of the Suprachiasmatic Nucleus including a persistent Na+ channel (Paul et al 2016) | |

Hodgkin-Huxley style model for a neuron of the Suprachiasmatic Nucleus (SCN). Modified from DeWoskin et al, PNAS, 2015 to include a persistent sodium current. The model is used to study the role of the kinase GSK3 in regulating the electrical activity of SCN neurons through a persistent sodium current. | ||

137. | Hierarchical anti-Hebbian network model for the formation of spatial cells in 3D (Soman et al 2019) | |

This model shows how spatial representations in 3D space could emerge using unsupervised neural networks. Model is a hierarchical one which means that it has multiple layers, where each layer has got a specific function to achieve. This architecture is more of a generalised one i.e. it gives rise to different kinds of spatial representations after training. | ||

138. | Hierarchical Gaussian Filter (HGF) model of conditioned hallucinations task (Powers et al 2017) | |

This is an instantiation of the Hierarchical Gaussian Filter (HGF) model for use with the Conditioned Hallucinations Task. | ||

139. | Hippocampal CA3 network and circadian regulation (Stanley et al. 2013) | |

This model produces the hippocampal CA3 neural network model used in the paper below. It has two modes of operation, a default mode and a circadian mode. In the circadian mode, parameters are swept through a range of values. This model can be quite easily adapted to produce theta and gamma oscillations, as certain parameter sweeps will reveal (see Figures). BASH scripts interact with GENESIS 2.3 to implement parameter sweeps. The model contains four cell types derived from prior papers. CA3 pyramidal are derived from Traub et al (1991); Basket, stratum oriens (O-LM), and Medial Septal GABAergic (MSG) interneurons are taken from Hajos et al (2004). | ||

140. | Hippocampal spiking model for context dependent behavior (Raudies & Hasselmo 2014) | |

Our model simulates the effect of context dependent behavior using discrete inputs to drive spiking activity representing place and item followed sequentially by a discrete representation of the motor actions involving a response to an item (digging for food) or the movement to a different item (movement to a different pot for food). This simple network was able to consistently learn the context-dependent responses. | ||

141. | Hodgkin–Huxley model with fractional gating (Teka et al. 2016) | |

We use fractional order derivatives to model the kinetic dynamics of the gate variables for the potassium and sodium conductances of the Hodgkin-Huxley model. Our results show that power-law dynamics of the different gate variables result in a wide range of action potential shapes and spiking patterns, even in the case where the model was stimulated with constant current. As a consequence, power-law behaving conductances result in an increase in the number of spiking patterns a neuron can generate and, we propose, expand the computational capacity of the neuron. | ||

142. | Homeostatic synaptic plasticity (Rabinowitch and Segev 2006a,b) | |

(2006a): "We investigated analytically and numerically the interplay between two opposing forms of synaptic plasticity: positive-feedback, long-term potentiation/depression (LTP/LTD), and negative-feedback, homeostatic synaptic plasticity (HSP). A detailed model of a CA1 pyramidal neuron, with numerous HSP-modifiable dendritic synapses, demonstrates that HSP may have an important role in selecting which spatial patterns of LTP/LTD are to last. ... Despite the negative-feedback nature of HSP, under both local and global HSP, numerous synaptic potentiations/depressions can persist. These experimentally testable results imply that HSP could be significantly involved in shaping the spatial distribution of synaptic weights in the dendrites and not just normalizing it, as is currently believed." (2006b): "Homeostatic synaptic plasticity (HSP) is an important mechanism attributed with the slow regulation of the neuron's activity. Whenever activity is chronically enhanced, HSP weakens the weights of the synapses in the dendrites and vice versa. Because dendritic morphology and its electrical properties partition the dendritic tree into functional compartments, we set out to explore the interplay between HSP and dendritic compartmentalization. ... The spatial distribution of synaptic weights throughout the dendrites will markedly differ under the local versus global HSP mechanisms. We suggest an experimental paradigm to unravel which type of HSP mechanism operates in the dendritic tree. The answer to this question will have important implications to our understanding of the functional organization of the neuron." | ||

143. | Hotspots of dendritic spine turnover facilitates new spines and NN sparsity (Frank et al 2018) | |

Model for the following publication: Adam C. Frank, Shan Huang, Miou Zhou, Amos Gdalyahu, George Kastellakis, Panayiota Poirazi, Tawnie K. Silva, Ximiao Wen, Joshua T. Trachtenberg, and Alcino J. Silva Hotspots of Dendritic Spine Turnover Facilitate Learning-related Clustered Spine Addition and Network Sparsity | ||

144. | Human auditory periphery model: cochlea, IHC-AN, auditory brainstem responses (Verhulst et al 2018) | |

The human auditory periphery model can simulate single-unit response of basilar-membrane vibration, IHC receptor potential, instantaneous AN/CN/IC firing rates, as well as population responses such as otoacoustic emissions, auditory brainstem responses. The neuron models (IHC, AN,CN,IC) can be run independently to relate their responses to electrophysiology, or be simulated as part of the human auditory periphery. | ||

145. | Human sleep-wake regulatory network model (Gleit et al 2013, Booth et al 2017) | |

A physiologically-based mathematical model of a sleep-wake regulatory network model for human sleep. The model simulates neurotransmitter-mediated interactions among hypothalamic and brainstem neuronal populations that promote wake, rapid eye movement (REM) sleep and non-REM (NREM) sleep. A neuronal population firing rate model formalism is used. The circadian rhythm pacemaker neuronal population, the suprachiasmatic nucleus (SCN), modulates activity in the wake- and sleep-promoting populations to entrain sleep-wake behavior to the ~24h circadian rhythm. A circadian clock oscillator model drives a 24h variation in the SCN firing rate and can be entrained to an externally imposed light:dark cycle. The default parameters replicate typical human sleep entrained to an external 14h:10h light:dark cycle | ||

146. | Human tactile FA1 neurons (Hay and Pruszynski 2020) | |

"... we show that synaptic integration across the complex signals from the first-order neuronal population could underlie human ability to accurately (< 3°) and rapidly process the orientation of edges moving across the fingertip. We first derive spiking models of human first-order tactile neurons that fit and predict responses to moving edges with high accuracy. We then use the model neurons in simulating the peripheral neuronal population that innervates a fingertip. We train classifiers performing synaptic integration across the neuronal population activity, and show that synaptic integration across first-order neurons can process edge orientations with high acuity and speed. ... our models suggest that integration of fast-decaying (AMPA-like) synaptic inputs within short timescales is critical for discriminating fine orientations, whereas integration of slow-decaying (NMDA-like) synaptic inputs supports discrimination of coarser orientations and maintains robustness over longer timescales" | ||

147. | Hybrid oscillatory interference / continuous attractor NN of grid cell firing (Bush & Burgess 2014) | |

Matlab code to simulate a hybrid oscillatory interference - continuous attractor network model of grid cell firing in pyramidal and stellate cells of rodent medial entorhinal cortex | ||

148. | Hyperbolic model (Daneshzand et al 2017) | |

A modified Izhikevich neuron model to address the switching patterns of neuronal firing seen in Parkinson's Disease. | ||

149. | Hyperconnectivity, slow synapses in PFC mental retardation and autism model (Testa-Silva et al 2011) | |

The subdirectory 'matlab' contains MATLAB scripts (The Mathworks, USA) that can be used to reproduce the panels of Figures 4-5. This directory contains files to reproduce sample computer simulations presented in the 2011 paper authored by Meredith, R., Testa-Silva, G., Loebel, A., Giugliano, M., de Kock, C.; Mansvelder, H. "Hyperconnectivity and slow synapses in prefrontal cortex of a model for mental retardation and autism". ABSTRACT "... We propose that these findings are tightly linked: using a network model, we show that slower synapses are essential to counterbalance hyperconnectivity in order to maintain a dynamic range of excitatory activity. However, the slow synaptic time constants induce decreased responsiveness to low frequency stimulation, which may explain deficits in integration and information processing in attentional neuronal networks in neurodevelopmental disorders." | ||

150. | I&F recurrent networks with current- or conductance-based synapses (Cavallari et al. 2014) | |

Recurrent networks of two populations (excitatory and inhibitory) of randomly connected Leaky Integrate-and-Fire (LIF) neurons with either current- or conductance-based synapses from the paper S. Cavallari, S. Panzeri and A. Mazzoni (2014) | ||

151. | IA and IT interact to set first spike latency (Molineux et al 2005) | |

Using patch clamp and modeling, we illustrate that spike latency characteristics are the product of an interplay between I(A) and low-threshold calcium current (I(T)) that requires a steady-state difference in the inactivation parameters of the currents. Furthermore, we show that the unique first-spike latency characteristics of stellate cells have important implications for the integration of coincident IPSPs and EPSPs, such that inhibition can shift first-spike latency to differentially modulate the probability of firing. | ||

152. | Impact of dendritic size and topology on pyramidal cell burst firing (van Elburg and van Ooyen 2010) | |

The code provided here was written to systematically investigate which of the physical parameters controlled by dendritic morphology underlies the differences in spiking behaviour observed in different realizations of the 'ping-pong'-model. Structurally varying dendritic topology and length in a simplified model allows us to separate out the physical parameters derived from morphology underlying burst firing. To perform the parameter scans we created a new NEURON tool the MultipleRunControl which can be used to easily set up a parameter scan and write the simulation results to file. Using this code we found that not input conductance but the arrival time of the return current, as measured provisionally by the average electrotonic path length, determines whether the pyramidal cell (with ping-pong model dynamics) will burst or fire single spikes. | ||

153. | INa and IKv4.3 heterogeneity in canine LV myocytes (Flaim et al 2006) | |

"The roles of sustained components of INa and IKv43 in shaping the action potentials (AP) of myocytes isolated from the canine left ventricle (LV) have not been studied in detail. Here we investigate the hypothesis that these two currents can contribute substantially to heterogeneity of early repolarization and arrhythmic risk.... The resulting simulations illustrate ways in which KChIP2- and Ca2+- dependent control of IKv43 can result in a sustained outward current that can neutralize INaL in a rate- and myocyte subtype-dependent manner. Both these currents appear to play significant roles in modulating AP duration and rate dependence in midmyocardial myocytes. ... By design, these models allow upward integration into organ models or may be used as a basis for further investigations into cellular heterogeneities." See paper for more and details. | ||

154. | Inferring connection proximity in electrically coupled networks (Cali et al. 2007) | |

In order to explore electrical coupling in the nervous system and its network-level organization, it is imperative to map the electrical synaptic microcircuits, in analogy with in vitro studies on monosynaptic and disynaptic chemical coupling. However, walking from cell to cell over large distances with a glass pipette is challenging, and microinjection of (fluorescent) dyes diffusing through gap-junctions remains so far the only method available to decipher such microcircuits even though technical limitations exist. Based on circuit theory, we derived analytical descriptions of the AC electrical coupling in networks of isopotential cells. We then proposed an operative electrophysiological protocol to distinguish between direct electrical connections and connections involving one or more intermediate cells. This method allows inferring the number of intermediate cells, generalizing the conventional coupling coefficient, which provides limited information. We provide here some analysis and simulation scripts that used to test our method through computer simulations, in vitro recordings, theoretical and numerical methods. Key words: Gap-Junctions; Electrical Coupling; Networks; ZAP current; Impedance. | ||

155. | Inhibition perturbations reveals dynamical structure of neural processing (Sadeh & Clopath 2020) | |

"Perturbation of neuronal activity is key to understanding the brain's functional properties, however, intervention studies typically perturb neurons in a nonspecific manner. Recent optogenetics techniques have enabled patterned perturbations, in which specific patterns of activity can be invoked in identified target neurons to reveal more specific cortical function. Here, we argue that patterned perturbation of neurons is in fact necessary to reveal the specific dynamics of inhibitory stabilization, emerging in cortical networks with strong excitatory and inhibitory functional subnetworks, as recently reported in mouse visual cortex. We propose a specific perturbative signature of these networks and investigate how this can be measured under different experimental conditions. Functionally, rapid spontaneous transitions between selective ensembles of neurons emerge in such networks, consistent with experimental results. Our study outlines the dynamical and functional properties of feature-specific inhibitory-stabilized networks, and suggests experimental protocols that can be used to detect them in the intact cortex." | ||

156. | Inhibitory control of motoneuron excitability (Venugopal et al 2011) | |

A two-compartment model for a motor neuron following chronic spinal cord injury with excessive dendritic persistent Ca2+ current. | ||

157. | Inhibitory plasticity balances excitation and inhibition (Vogels et al. 2011) | |

"Cortical neurons receive balanced excitatory and inhibitory synaptic currents. Such a balance could be established and maintained in an experience-dependent manner by synaptic plasticity at inhibitory synapses. We show that this mechanism provides an explanation for the sparse firing patterns observed in response to natural stimuli and fits well with a recently observed interaction of excitatory and inhibitory receptive field plasticity. ... Our results suggest an essential role of inhibitory plasticity in the formation and maintenance of functional cortical circuitry." | ||

158. | Inner hair cell auditory nerve synapse model (Deligeorges, Mountain 1997) | |

This model simulates the response of the synapse between the inner hair cell and an auditory nerve fiber to a square voltage pulse applied to the IHC membrane. The model output is average firing rate. More details of this model can be found in: Deligeorges and Mountain. | ||

159. | Input strength and time-varying oscillation peak frequency (Cohen MX 2014) | |

The purpose of this paper is to argue that a single neural functional principle—temporal fluctuations in oscillation peak frequency (“frequency sliding”)—can be used as a common analysis approach to bridge multiple scales within neuroscience. The code provided here recreates the network models used to demonstrate changes in peak oscillation frequency as a function of static and time-varying input strength, and also shows how correlated frequency sliding can be used to identify functional connectivity between two networks. | ||

160. | Inverse stochastic resonance of cerebellar Purkinje cell (Buchin et al. 2016) | |

This code shows the simulations of the adaptive exponential integrate-and-fire model (http://www.scholarpedia.org/article/Adaptive_exponential_integrate-and-fire_model) at different stimulus conditions. The parameters of the model were tuned to the Purkinje cell of cerebellum to reproduce the inhibiion of these cells by noisy current injections. Similar experimental protocols were also applied to the detailed biophysical model of Purkinje cells, de Shutter & Bower (1994) model. The repository also includes the XPPaut version of the model with the corresponding bifurcation analysis. | ||

161. | Investigation of different targets in deep brain stimulation for Parkinson`s (Pirini et al. 2009) | |

"We investigated by a computational model of the basal ganglia the different network effects of deep brain stimulation (DBS) for Parkinson’s disease (PD) in different target sites in the subthalamic nucleus (STN), the globus pallidus pars interna (GPi), and the globus pallidus pars externa (GPe). A cellular-based model of the basal ganglia system (BGS), based on the model proposed by Rubin and Terman (J Comput Neurosci 16:211–235, 2004), was developed. ... Our results suggest that DBS in the STN could functionally restore the TC relay activity, while DBS in the GPe and in the GPi could functionally over-activate and inhibit it, respectively. Our results are consistent with the experimental and the clinical evidences on the network effects of DBS." | ||

162. | Irregular spiking in NMDA-driven prefrontal cortex neurons (Durstewitz and Gabriel 2006) | |

Slow N-Methyl-D-aspartic acid (NMDA) synaptic currents are assumed to strongly contribute to the persistently elevated firing rates observed in prefrontal cortex (PFC) during working memory. During persistent activity, spiking of many neurons is highly irregular. ... The highest interspike-interval (ISI) variability occurred in a transition regime where the subthreshold membrane potential distribution shifts from mono- to bimodality, ... Predictability within irregular ISI series was significantly higher than expected from a noise-driven linear process, indicating that it might best be described through complex (potentially chaotic) nonlinear deterministic processes. Accordingly, the phenomena observed in vitro could be reproduced in purely deterministic biophysical model neurons. High spiking irregularity in these models emerged within a chaotic, close-to-bifurcation regime characterized by a shift of the membrane potential distribution from mono- to bimodality and by similar ISI return maps as observed in vitro. ... NMDA-induced irregular dynamics may have important implications for computational processes during working memory and neural coding. | ||

163. | L5 cortical neurons with recreated synaptic inputs in vitro correlation transfer (Linaro et al 2019) | |

"...We studied pyramidal neurons and two classes of GABAergic interneurons of layer 5 in neocortical brain slices obtained from rats of both sexes, and we stimulated them with biophysically realistic correlated inputs, generated using dynamic clamp. We found that the physiological differences between cell types manifested unique features in their capacity to transfer correlated inputs. We used linear response theory and computational modeling to gain clear insights into how cellular properties determine both the gain and timescale of correlation transfer, thus tying single-cell features with network interactions. Our results provide further ground for the functionally distinct roles played by various types of neuronal cells in the cortical microcircuit..." | ||

164. | L5 pyr. cell spiking control by oscillatory inhibition in distal apical dendrites (Li et al 2013) | |

This model examined how distal oscillatory inhibition influences the firing of a biophysically-detailed layer 5 pyramidal neuron model. | ||

165. | Laminar analysis of excitatory circuits in vibrissal motor and sensory cortex (Hooks et al. 2011) | |

"... We mapped local excitatory pathways in each area (primary motor cortex (vM1), primary somatosensory cortex (vS1; barrel cortex), and secondary somatosensory cortex (S2)) across all cortical layers using glutamate uncaging and laser scanning photostimulation. We analyzed these maps to derive laminar connectivity matrices describing the average strengths of pathways between individual neurons in different layers and between entire cortical layers. ..." | ||

166. | Laminar connectivity matrix simulation (Weiler et al 2008) | |

A routine that simulates the flow of activity within and across laminar levels in the local pyramidal neuron network, based on a connectivity matrix (W) measured by laser scanning photostimulation in mouse somatic motor cortex, and a very simple neural network simulation. | ||

167. | Large-scale laminar model of macaque cortex (Mejias et al 2016) | |

This code reproduces the large-scale cortical model with laminar structure presented in Mejias et al., Science Advances 2016. The model includes different scales (intra-laminar, inter-laminar, inter-areal and large-scale) across macaque neocortex and reproduces experimentally observed dynamics of gamma and alpha/beta oscillations across these scales. It makes use of real anatomical data from the macaque cortex. Some parts of the code require external packages or data (see readme file for details). | ||

168. | Lateral dendrodenditic inhibition in the Olfactory Bulb (David et al. 2008) | |

Mitral cells, the principal output neurons of the olfactory bulb, receive direct synaptic activation from primary sensory neurons. Shunting inhibitory inputs delivered by granule cell interneurons onto mitral cell lateral dendrites are believed to influence spike timing and underlie coordinated field potential oscillations. Lateral dendritic shunt conductances delayed spiking to a degree dependent on both their electrotonic distance and phase of onset. Recurrent inhibition significantly narrowed the distribution of mitral cell spike times, illustrating a tendency towards coordinated synchronous activity. This result suggests an essential role for early mechanisms of temporal coordination in olfaction. The model was adapted from Davison et al, 2003, but include additional noise mechanisms, long lateral dendrite, and specific synaptic point processes. | ||

169. | Layer 5 Pyramidal Neuron (Shai et al., 2015) | |

This work contains a NEURON model for a layer 5 pyramidal neuron (based on Hay et al., 2011) with distributed groups of synapses across the basal and tuft dendrites. The results of that simulation are used to fit a phenomenological model, which is also included in this file. | ||

170. | Leaky integrate-and-fire model of spike frequency adaptation in the LGMD (Gabbiani and Krapp 2006) | |

This will reproduce Figure 9 of Gabbiani and Krapp (2006) J Neurophysiol 96:2951-2962. The figure simply shows that a leaky-integrate-and-fire model cannot reproduce spike frequency adaptation as it is seen experimentally in the LGMD neuron. | ||

171. | Learning intrinsic excitability in Medium Spiny Neurons (Scheler 2014) | |

"We present an unsupervised, local activation-dependent learning rule for intrinsic plasticity (IP) which affects the composition of ion channel conductances for single neurons in a use-dependent way. We use a single-compartment conductance-based model for medium spiny striatal neurons in order to show the effects of parameterization of individual ion channels on the neuronal membrane potential-curent relationship (activation function). We show that parameter changes within the physiological ranges are sufficient to create an ensemble of neurons with significantly different activation functions. ... " | ||

172. | Learning spatiotemporal sequences using recurrent spiking NN that discretizes time (Maes et al 2020) | |

"Learning to produce spatiotemporal sequences is a common task that the brain has to solve. The same neural substrate may be used by the brain to produce different sequential behaviours. The way the brain learns and encodes such tasks remains unknown as current computational models do not typically use realistic biologically-plausible learning. Here, we propose a model where a spiking recurrent network of excitatory and inhibitory biophysical neurons drives a read-out layer: the dynamics of the driver recurrent network is trained to encode time which is then mapped through the read-out neurons to encode another dimension, such as space or a phase. Different spatiotemporal patterns can be learned and encoded through the synaptic weights to the read-out neurons that follow common Hebbian learning rules. We demonstrate that the model is able to learn spatiotemporal dynamics on time scales that are behaviourally relevant and we show that the learned sequences are robustly replayed during a regime of spontaneous activity." | ||

173. | Leech Heart Interneuron model (Sharma et al 2020) | |

Fractional order Leech heart interneuron model is investigated. Different firing properties are explored. In this article, we investigate the alternation of spiking and bursting phenomena of an uncoupled and coupled fractional Leech-Heart (L-H) neurons. We show that a complete graph of heterogeneous de-synchronized neurons in the backdrop of diverse memory settings (a mixture of integer and fractional exponents) can eventually lead to bursting with the formation of cluster synchronization over a certain threshold of coupling strength, however, the uncoupled L-H neurons cannot reveal bursting dynamics. Using the stability analysis in fractional domain, we demarcate the parameter space where the quiescent or steady-state emerges in uncoupled L-H neuron. Finally, a reduced-order model is introduced to capture the activities of the large network of fractional-order model neurons. | ||

174. | LGMD Variability and logarithmic compression in dendrites (Jones and Gabbiani, 2012, 2012B) | |

A compartmental model of the LGMD with a simplified, rake shaped, excitatory dendrite. It receives spontaneous input and excitatory and inhibitory synaptic inputs triggered by visual stimuli. It generates realistic responses to looming through the velocity dependent scaling and delay of individual excitatory synaptic inputs, with variability. We use the model to show that the key determinants of output variability are spontaneous input and temporal jitter of the excitatory inputs, rather than variability in magnitude of individual inputs (2012B, J Neurophysiol). We also use the model to analyze the transformation of the excitatory signals through the visual pathway; concluding that the representation of stimulus velocity is transformed from an expansive relationship at the level of the LGMD inputs to a logarithmic one at the level of its membrane potential (2012, J Neurosci). | ||

175. | LGNcircuit: Minimal LGN network model of temporal processing of visual input (Norheim et al. 2012) | |

The responses of relay cells in the lateral geniculate nucleus (LGN) are shaped by their diverse set of impinging inputs: feedforward synaptic inputs stemming from retina, and feedback inputs stemming from the visual cortex and the thalamic reticular nucleus. This MATLAB model, with an easy-to-use graphical user interface (GUI), explores possible roles of these feedforward and feedback inputs in shaping the temporal part of the receptive fields of LGN relay cells with, so called, ON symmetry. A minimal mechanistic firing-rate model tailored to elucidate salient feedforward and feedback effects is considered including, in particular, feedforward excitation and inhibition (via interneurons) from retinal ON cells and excitatory and inhibitory (via thalamic reticular nucleus cells and interneurons) feedback from cortical ON and OFF cells. Various types of visual stimuli can be explored: flashing spots, impulses, sinusoidal gratings. | ||

176. | Lillie Transition: onset of saltatory conduction in myelinating axons (Young et al. 2013) | |

Included are the NEURON (.hoc) files needed to generate the data used in our Young, Castelfranco, Hartline (2013) paper. The resulting .dat files are in the same folder as the MATLAB (.m) files that are used to sort the data. | ||

177. | Linking STDP and Dopamine action to solve the distal reward problem (Izhikevich 2007) | |

"... How does the brain know what firing patterns of what neurons are responsible for the reward if 1) the patterns are no longer there when the reward arrives and 2) all neurons and synapses are active during the waiting period to the reward? Here, we show how the conundrum is resolved by a model network of cortical spiking neurons with spike-timing-dependent plasticity (STDP) modulated by dopamine (DA). Although STDP is triggered by nearly coincident firing patterns on a millisecond timescale, slow kinetics of subsequent synaptic plasticity is sensitive to changes in the extracellular DA concentration during the critical period of a few seconds. ... This study emphasizes the importance of precise firing patterns in brain dynamics and suggests how a global diffusive reinforcement signal in the form of extracellular DA can selectively influence the right synapses at the right time." See paper for more and details. | ||

178. | Logarithmic distributions prove that intrinsic learning is Hebbian (Scheler 2017) | |

"In this paper, we present data for the lognormal distributions of spike rates, synaptic weights and intrinsic excitability (gain) for neurons in various brain areas, such as auditory or visual cortex, hippocampus, cerebellum, striatum, midbrain nuclei. We find a remarkable consistency of heavy-tailed, specifically lognormal, distributions for rates, weights and gains in all brain areas examined. The difference between strongly recurrent and feed-forward connectivity (cortex vs. striatum and cerebellum), neurotransmitter (GABA (striatum) or glutamate (cortex)) or the level of activation (low in cortex, high in Purkinje cells and midbrain nuclei) turns out to be irrelevant for this feature. Logarithmic scale distribution of weights and gains appears to be a general, functional property in all cases analyzed. We then created a generic neural model to investigate adaptive learning rules that create and maintain lognormal distributions. We conclusively demonstrate that not only weights, but also intrinsic gains, need to have strong Hebbian learning in order to produce and maintain the experimentally attested distributions. This provides a solution to the long-standing question about the type of plasticity exhibited by intrinsic excitability." | ||

179. | Macroscopic model of epilepsy (Fietkiewicz & Loparo 2016) | |

Simulates epileptiform EEG. Original model used for Figure 2 in Fietkiewicz and Loparo 2016. The MATLAB program uses Euler integration to create the basic plot in Figure 2. The model is based on the original model specified in Wendling F, Bartolomei F, Bellanger JJ, Chauvel P. Epileptic fast activity can be explained by a model of impaired GABAergic dendritic inhibition. Eur J Neurosci, 2002;15(9):1499-1508. | ||

180. | Markov Chain-based Stochastic Shielding Hodgkin Huxley Model (Schmandt, Galan 2012) | |

181. | Mathematical model for windup (Aguiar et al. 2010) | |

"Windup is characterized as a frequency-dependent increase in the number of evoked action potentials in dorsal horn neurons in response to electrical stimulation of afferent C-fibers. ... The approach presented here relies on mathematical and computational analysis to study the mechanism(s) underlying windup. From experimentally obtained windup profiles, we extract the time scale of the facilitation mechanisms that may support the characteristics of windup. Guided by these values and using simulations of a biologically realistic compartmental model of a wide dynamic range (WDR) neuron, we are able to assess the contribution of each mechanism for the generation of action potentials windup. ..." | ||

182. | Maximum entropy model to predict spatiotemporal spike patterns (Marre et al. 2009) | |

This MATLAB code implements a model-based analysis of spike trains. The analysis predicts the occurrence of spatio-temporal patterns of spikes in the data, and is based on a maximum entropy principle by including both spatial and temporal correlations. The approach is applicable to unit recordings from any region of the brain. The code is based on Marre, et al., 2009. The MATLAB code was written by Sami El Boustani and Olivier Marre. | ||

183. | Mean Field Equations for Two-Dimensional Integrate and Fire Models (Nicola and Campbell, 2013) | |

The zip file contains the files used to perform numerical simulation and bifurcation studies of large networks of two-dimensional integrate and fire neurons and of the corresponding mean field models derived in our paper. The neural models used are the Izhikevich model and the Adaptive Exponential model. | ||

184. | Mean-field systems and small scale neural networks (Ferguson et al. 2015) | |

We explore adaptation induced bursting as a mechanism for theta oscillations in hippocampal area CA1. To do this, we have developed a mean-field system for a network of fitted Izhikevich neurons with sparse coupling and heterogeneity. The code contained here runs the mean-field system pointwise or on a two-parameter mesh, in addition to networks of neurons that are smaller then those considered in the paper. The file README.pdf contains instructions on use. Note that the following file (peakfinder): http://www.mathworks.com/matlabcentral/fileexchange/25500-peakfinder-x0--sel--thresh--extrema--includeendpoints--interpolate- is required to compute burst frequencies in the mean-field system and must be downloaded and placed in the same root folder as MFSIMULATOR.mat | ||

185. | Mechanisms of magnetic stimulation of central nervous system neurons (Pashut et al. 2011) | |

Transcranial magnetic stimulation (TMS) is a widely applied tool for probing cognitive function in humans and is one of the best tools for clinical treatments and interfering with cognitive tasks. Surprisingly, while TMS has been commercially available for decades, the cellular mechanisms underlying magnetic stimulation remain unclear. Here we investigate these mechanisms using compartmental modeling. We generated a numerical scheme allowing simulation of the physiological response to magnetic stimulation of neurons with arbitrary morphologies and active properties. Computational experiments using this scheme suggested that TMS affects neurons in the central nervous system (CNS) primarily by somatic stimulation. | ||

186. | Mechanisms underlying different onset patterns of focal seizures (Wang Y et al 2017) | |

"Focal seizures are episodes of pathological brain activity that appear to arise from a localised area of the brain. The onset patterns of focal seizure activity have been studied intensively, and they have largely been distinguished into two types { low amplitude fast oscillations (LAF), or high amplitude spikes (HAS). Here we explore whether these two patterns arise from fundamentally different mechanisms. Here, we use a previously established computational model of neocortical tissue, and validate it as an adequate model using clinical recordings of focal seizures. We then reproduce the two onset patterns in their most defining properties and investigate the possible mechanisms underlying the different focal seizure onset patterns in the model. ..." | ||

187. | Medial reticular formation of the brainstem: anatomy and dynamics (Humphries et al. 2006, 2007) | |

A set of models to study the medial reticular formation (mRF) of the brainstem. We developed a collection of algorithms to derive the adult-state wiring of the model: one set a stochastic model; the other set mimicking the developmental process. We found that the anatomical models had small-world properties, irrespective of the choice of algorithm; and that the cluster-like organisation of the mRF may have arisen to minimise wiring costs. (The model code includes options to be run as dynamic models; papers examining these dynamics are included in the .zip file). | ||

188. | Memory savings through unified pre- and postsynaptic STDP (Costa et al 2015) | |

Although it is well known that long-term synaptic plasticity can be expressed both pre- and postsynaptically, the functional consequences of this arrangement have remained elusive. We show that spike-timing-dependent plasticity with both pre- and postsynaptic expression develops receptive fields with reduced variability and improved discriminability compared to postsynaptic plasticity alone. These long-term modifications in receptive field statistics match recent sensory perception experiments. In these simulations we demonstrate that learning with this form of plasticity leaves a hidden postsynaptic memory trace that enables fast relearning of previously stored information, providing a cellular substrate for memory savings. Our results reveal essential roles for presynaptic plasticity that are missed when only postsynaptic expression of long-term plasticity is considered, and suggest an experience-dependent distribution of pre- and postsynaptic strength changes. | ||

189. | Method for deriving general HH neuron model`s spiking input-output relation (Soudry & Meir 2014) | |

We derived in paper a method to find semi-analytic input-output relations for general HH-like neuron models (firing rates, spectra, linear filters)under sparse spike stimulation. Here we demonstrate the applicability of this method to various HH-type models (HH with slow sodium inactivation, with slow pottasium inactivation, with synaptic STD and other various extensions). | ||

190. | Microglial cytokine network (Anderson et al., 2015) | |

This is an ODE model of autocrine/paracrine microglial cytokine interactions. Simulations include analyses of neuroinflammation mechanisms in the context of adaptation and tolerance to LPS. | ||

191. | Microsaccades and synchrony coding in the retina (Masquelier et al. 2016) | |

We show that microsaccades (MS) enable efficient synchrony-based coding among the primate retinal ganglion cells (RGC). We find that each MS causes certain RGCs to fire synchronously, namely those whose receptive fields contain contrast edges after the MS. The emitted synchronous spike volley thus rapidly transmits the most salient edges of the stimulus. We demonstrate that the readout could be done rapidly by simple coincidence-detector neurons, and that the required connectivity could emerge spontaneously with spike timing-dependent plasticity. | ||

192. | Midbrain torus semicircularis neuron model (Aumentado-Armstrong et al. 2015) | |

This paper investigates how midbrain electrosensory neurons give invariant responses to natural communication stimuli. A model explains that such invariance can be achieved by combining afferent input from ON and OFF cells. | ||

193. | Model for concentration invariant odor coding based on primacy hypothesis (Wilson et al 2017) | |

"... Here we propose that, in olfaction, a small and relatively stable set comprised of the earliest activated receptors forms a code for concentration-invariant odor identity. One prediction of this “primacy coding” scheme is that decisions based on odor identity can be made solely using early odor-evoked neural activity. Using an optogenetic masking paradigm, we define the sensory integration time necessary for odor identification and demonstrate that animals can use information occurring <100ms after inhalation onset to identify odors. ... We propose a computational model demonstrating how such a code can be read by neural circuits of the olfactory system." | ||

194. | Model of AngII signaling and membrane electrophysiology (Makadia, Anderson, Fey et al., 2015) | |

We developed a novel multiscale model to bridge neuropeptide receptor-activated signaling pathway with membrane electrophysiology. The model studies the effects of Angiotensin II (AngII) on neuronal excitability changes mediated by signaling dynamics and downstream phosphorylation of ion channels. The multiscale model was implemented as a set of ordinary differential equations solved using the ode15s solver in Matlab (Mathworks, USA). The signaling reactions were modeled with either mass-action or Michaelis--Menten kinetics and ion channel electrophysiology was modeled according to the Hodgkin-Huxley formalism. These models were initially validated against their respective data domains independently and were integrated to develop a multiscale model of signaling and electrophysiology. | ||

195. | Model of calcium oscillations in olfactory cilia (Reidl et al. 2006) | |

Simulation of experiments on olfactory receptor neurons (ORNs). Focussing on the negative feedback that calcium (through calmodulin) has on its own influx through CNG channels, this model is able to reproduce both calcium oscillations as well as adaptation behaviour as seen in experiments done with ORNs. | ||

196. | Model of cerebellar parallel fiber-Purkinje cell LTD and LTP (Gallimore et al 2018) | |

Model of cerebellar parallel fiber-Purkinje cell LTD and LTP implemented in Matlab Simbiology | ||

197. | Model of cochlear membrane adapted (Peterson, Bogert 1950) | |

This model, adapted from Peterson and Bogert (1950), simulates the response of the gerbil basilar membrane to a pure tone stimulus. This model does not attempt to simulate the effect of outer hair cell motility. The program prompts the user for the stimulus frequency and the Q (quality factor) for the basilar membrane impedance. It then plots cochlear partition volume velocity, the pressure difference across the partition and the cochlear partition impedance as a function of cochlear location. More information on the actual computations are contained in comments within the m-file. | ||

198. | Model of long range transmission of gamma oscillation (Murray 2007) | |

"... A minimal mathematical model was developed for a preliminary study of long-range neural transmission of gamma oscillation from the CA3 to the entorhinal cortex via the CAI region of the hippocampus, a subset within a larger complex set of pathways. A module was created for each local population of neurons with common intrinsic properties and connectivity to simplify the connection process and make the model more flexible. Three modules were created using MATLAB Simulink® and tested to confirm that they transmit gamma through the system. The model also revealed that a portion of the signal from CAI to the entorhinal cortex may be lost in transmission under certain conditions." | ||

199. | Model of the Xenopus tadpole swimming spinal network (Roberts et al. 2014) | |

This is a NEURON-python and MATLAB simulation code for generating anatomical or probabilistic connectivity and simulating the neuronal dynamics of the neuronal network controlling swimming in Xenopus tadpoles. For more details about this model, see Ferrario et al, 2018, eLife and Roberts et al, 2014, J of Neurosci | ||

200. | Model of working memory based on negative derivative feedback (Lim and Goldman, 2013) | |

We proposed a model of working memory in which recurrent synaptic interactions provide a corrective feedback that enables persistent activity to be maintained stably for prolonged durations. When recurrent excitatory and inhibitory inputs to memory neurons were balanced in strength and offset in time, drifts in activity triggered a corrective signal that counteracted memory decay. Circuits containing this mechanism temporally integrated their inputs, generated the irregular neural firing observed during persistent activity and were robust against common perturbations that severely disrupted previous models of short-term memory storage. | ||

201. | Model predictive control model for an isometric motor task (Ueyama 2017) | |

A model predictive control model for an isometric motor task. | ||

202. | Modeling and MEG evidence of early consonance processing in auditory cortex (Tabas et al 2019) | |

Pitch is a fundamental attribute of auditory perception. The interaction of concurrent pitches gives rise to a sensation that can be characterized by its degree of consonance or dissonance. In this work, we propose that human auditory cortex (AC) processes pitch and consonance through a common neural network mechanism operating at early cortical levels. First, we developed a new model of neural ensembles incorporating realistic neuronal and synaptic parameters to assess pitch processing mechanisms at early stages of AC. Next, we designed a magnetoencephalography (MEG) experiment to measure the neuromagnetic activity evoked by dyads with varying degrees of consonance or dissonance. MEG results show that dissonant dyads evoke a pitch onset response (POR) with a latency up to 36 ms longer than consonant dyads. Additionally, we used the model to predict the processing time of concurrent pitches; here, consonant pitch combinations were decoded faster than dissonant combinations, in line with the experimental observations. Specifically, we found a striking match between the predicted and the observed latency of the POR as elicited by the dyads. These novel results suggest that consonance processing starts early in human auditory cortex and may share the network mechanisms that are responsible for (single) pitch processing. | ||

203. | Modeling extracellular electrical stimulation (Tahayori et al. 2012) | |

"The validity of approximate equations describing the membrane potential under extracellular electrical stimulation (Meffin et al 2012 J. Neural Eng. 9 065005) is investigated through finite element analysis in this paper. To this end, the finite element method is used to simulate a cylindrical neurite under extracellular stimulation. Laplace's equations with appropriate boundary conditions are solved numerically in three dimensions and the results are compared to the approximate analytic solutions. ..." | ||

204. | Modeling hebbian and homeostatic plasticity (Toyoizumi et al. 2014) | |

"... We propose a model in which synaptic strength is the product of a synapse-specific Hebbian factor and a postsynaptic- cell-specific homeostatic factor, with each factor separately arriving at a stable inactive state. This model captures ODP dynamics and has plausible biophysical substrates. We confirm model predictions experimentally that plasticity is inactive at stable states and that synaptic strength overshoots during recovery from visual deprivation. ..." | ||

205. | Modelling enteric neuron populations and muscle fed-state motor patterns (Chambers et al. 2011) | |

"After a meal, the gastrointestinal tract exhibits a set of behaviours known as the fed state. ... Segmentation manifests as rhythmic local constrictions that do not propagate along the intestine. ... We investigated the enteric circuits that regulate segmentation focusing on a central feature of the ENS: a recurrent excitatory network of intrinsic sensory neurons (ISNs) which are characterized by prolonged after-hyperpolarizing potentials (AHPs) following their action potentials. ..." | ||

206. | Modelling gain modulation in stability-optimised circuits (Stroud et al 2018) | |

We supply Matlab code to create 'stability-optimised circuits'. These networks can give rise to rich neural activity transients that resemble primary motor cortex recordings in monkeys during reaching. We also supply code that allows one to learn new network outputs by changing the input-output gain of neurons in a stability-optimised network. Our code recreates the main results of Figure 1 in our related publication. | ||

207. | Models for cortical UP-DOWN states in a bistable inhibitory-stabilized network (Jercog et al 2017) | |

In the idling brain, neuronal circuits transition between periods of sustained firing (UP state) and quiescence (DOWN state), a pattern the mechanisms of which remain unclear. We analyzed spontaneous cortical population activity from anesthetized rats and found that UP and DOWN durations were highly variable and that population rates showed no significant decay during UP periods. We built a network rate model with excitatory (E) and inhibitory (I) populations exhibiting a novel bistable regime between a quiescent and an inhibition-stabilized state of arbitrarily low rate, where fluctuations triggered state transitions. In addition, we implemented these mechanisms in a more biophysically realistic spiking network, where DOWN-to-UP transitions are caused by synchronous high-amplitude events impinging onto the network. | ||

208. | Models of Vector Navigation with Grid Cells (Bush et al., 2015) | |

Four models of vector navigation in large scale 2D space using grid cell representations of location are included: (1) The 'Distance Cell' model, which directly decodes absolute start and goal locations in allocentric space from rate-coded grid cell representations before computing the displacement between them; (2) The 'Rate-coded Vector Cell' model, which directly decodes the displacement between start and goal locations from rate-coded grid cell representations; (3) The 'Phase-coded Vector Cell' model, which directly decodes the displacement between start and goal locations from the temporally-coded grid cell representations provided by phase precession; (4) The 'Linear Look-ahead' model, which uses a directed search through grid cell representations, initiated at the start location and then moving along a specific axis at a constant speed, to compute the displacement between start and goal locations. | ||

209. | Motoneuron model of self-sustained firing after spinal cord injury (Kurian et al. 2011) | |

" ... During the acute-stage of spinal cord injury (SCI), the endogenous ability to generate plateaus is lost; however, during the chronic-stage of SCI, plateau potentials reappear with prolonged self-sustained firing that has been implicated in the development of spasticity. In this work, we extend previous modeling studies to systematically investigate the mechanisms underlying the generation of plateau potentials in motoneurons, including the influences of specific ionic currents, the morphological characteristics of the soma and dendrite, and the interactions between persistent inward currents and synaptic input. ..." | ||

210. | Motoneuron simulations for counting motor units (Major and Jones 2005) | |

Simulations of clinical methods to count the number of motoneurons/motor units in human patients. Models include stimulation of motor axons or voluntary activation and responses are measured as muscle tension or EMG. | ||

211. | Multiple mechanisms of short term plasticity at the calyx of Held (Hennig et al. 2008) | |

This is a new model of the short-term dynamics of glutamatergic synaptic transmission, which incorporates multiple mechanisms acting at differing sites and across a range of different time scales (ms to tens of seconds). In the paper, we show that this model can accurately reproduce the experimentally measured time-course of short term depression across different stimulus frequencies at the calyx of Held. The model demonstrates how multiple forms of activity-dependent modulation of release probability and vesicle pool depletion interact, and shows how stimulus-history-dependent recovery from synaptic depression can arise from dynamics on multiple time scales. | ||

212. | Multiple modes of inner hair cell stimulation (Mountain, Cody 1999) | |

This model simulates the membrane potential of an inner hair cell for a sinusoidal stimulus to the hair bundle. It uses a 2-state Boltzmann model for the tension-gated conductance in the stereocilia and a linear model for the basolateral membrane. This model is based on the IHC model used in Mountain and Cody (1999). | ||

213. | Multisensory integration in the superior colliculus: a neural network model (Ursino et al. 2009) | |

" ... The model includes three distinct neural areas: two unimodal areas (auditory and visual) are devoted to a topological representation of external stimuli, and communicate via synaptic connections with a third downstream area (in the SC) responsible for multisensory integration. The present simulations show that the model, with a single set of parameters, can mimic various responses to different combinations of external stimuli including the inverse effectiveness, both in terms of multisensory enhancement and contrast, the existence of within- and cross-modality suppression between spatially disparate stimuli, a reduction of network settling time in response to cross-modal stimuli compared with individual stimuli. ..." | ||

214. | Neocort. pyramidal cells subthreshold somatic voltage controls spike propagation (Munro Kopell 2012) | |

There is suggestive evidence that pyramidal cell axons in neocortex may be coupled by gap junctions into an ``axonal plexus" capable of generating Very Fast Oscillations (VFOs) with frequencies exceeding 80 Hz. It is not obvious, however, how a pyramidal cell in such a network could control its output when action potentials are free to propagate from the axons of other pyramidal cells into its own axon. We address this problem by means of simulations based on 3D reconstructions of pyramidal cells from rat somatosensory cortex. We show that somatic depolarization enables propagation via gap junctions into the initial segment and main axon, while somatic hyperpolarization disables it. We show further that somatic voltage cannot effectively control action potential propagation through gap junctions on minor collaterals; action potentials may therefore propagate freely from such collaterals regardless of somatic voltage. In previous work, VFOs are all but abolished during the hyperpolarization phase of slow-oscillations induced by anesthesia in vivo. This finding constrains the density of gap junctions on collaterals in our model and suggests that axonal sprouting due to cortical lesions may result in abnormally high gap junction density on collaterals, leading in turn to excessive VFO activity and hence to epilepsy via kindling. | ||

215. | Network model of movement disorders (Yousif et al 2020) | |

This is a Wilson-Cowan model of the basal ganglia thalamocortical cerebellar network that demonstrates healthy gamma band oscillations, Parkinsonian oscillations in the beta band and oscillations in the tremor frequency range arising from the dynamics of the network. | ||

216. | Network models of frequency modulated sweep detection (Skorheim et al. 2014) | |

"Frequency modulated (FM) sweeps are common in species-specific vocalizations, including human speech. Auditory neurons selective for the direction and rate of frequency change in FM sweeps are present across species, but the synaptic mechanisms underlying such selectivity are only beginning to be understood. Even less is known about mechanisms of experience-dependent changes in FM sweep selectivity. We present three network models of synaptic mechanisms of FM sweep direction and rate selectivity that explains experimental data ... " | ||

217. | Neural mass model based on single cell dynamics to model pathophysiology (Zandt et al 2014) | |

The model code as described in "A neural mass model based on single cell dynamics to model pathophysiology, Zandt et al. 2014, Journal of Computational Neuroscience" A Neural mass model (NMM) derived from single cell dynamics in a bottom up approach. Mean and standard deviation of the firing rates in the populations are calculated. The sigmoid is derived from the single cell FI-curve, allowing for easy implementation of pathological conditions. NMM is compared with a detailed spiking network model consisting of HH neurons. NMM code in Matlab. The network model is simulated using Norns (ModelDB # 154739) | ||

218. | Neural mass model of the sleeping cortex (Weigenand et al 2014) | |

Generates typical EEG data of sleeping Humans for sleep stages N2/N3 as well as wakefulness | ||

219. | Neural model of frog ventilatory rhythmogenesis (Horcholle-Bossavit and Quenet 2009) | |

"In the adult frog respiratory system, periods of rhythmic movements of the buccal floor are interspersed by lung ventilation episodes. The ventilatory activity results from the interaction of two hypothesized oscillators in the brainstem. Here, we model these oscillators with two coupled neural networks, whose co-activation results in the emergence of new dynamics. .. The biological interest of this formal model is illustrated by the persistence of the relevant dynamical features when perturbations are introduced in the model, i.e. dynamic noises and architecture modifications. The implementation of the networks with clock-driven continuous time neurones provides simulations with physiological time scales." | ||

220. | Neural transformations on spike timing information (Tripp and Eliasmith 2007) | |

" ... Here we employ computational methods to show that an ensemble of neurons firing at a constant mean rate can induce arbitrarily chosen temporal current patterns in postsynaptic cells. ..." | ||

221. | Neural-field model of generalized seizures (Zhao et al., 2015) | |

The mechanisms underlying generalized seizures are explored with neural field theory. A corticothalamic neural field model is used to explore changes leading to pathological seizure states. It is found that absence seizures arise from instabilities in the system and replicate experimental studies in numerous animal models and clinical studies. | ||

222. | Neurogenesis in the olfactory bulb controlled by top-down input (Adams et al 2018) | |

This code implements a model for adult neurogenesis of granule cells in the olfactory system. The granule cells receive sensory input via the mitral cells and top-down input from a cortical area. That cortical area also receives olfactory input from the mitral cells as well as contextual input. This plasticity leads to a network structure consisting of bidirectional connections between bulbar and cortical odor representations. The top-down input enhances stimulus discrimination based on contextual input. | ||

223. | Non-Weak E-Fields Pyramidal Neurons (Reznik et. al.,2015) | |

Effect of Polarization Induced by Non-Weak Electric Fields on the Excitability of Elongated Neurons With Active Dendrite. In response to polarization, the active currents in the dendrites of pyramidal neurons play a pivotal role in the excitability of elongated neurons. Depending on a number of parameters either hyperpolarizing or depolarizing currents in the dendrite dominate as polarization is increased. Furthermore, the impact that these active dendrite channels (Ca, KAHP, etc) occur when only a small fraction of their channels are open. | ||

224. | Norns - Neural Network Studio (Visser & Van Gils 2014) | |

The Norns - Neural Network Studio is a software package for designing, simulation and analyzing networks of spiking neurons. It consists of three parts: 1. "Urd": a Matlab frontend with high-level functions for quickly defining networks 2. "Verdandi": an optimized C++ simulation environment which runs the simulation defined by Urd 3. "Skuld": an advanced Matlab graphical user interface (GUI) for visual inspection of simulated data. | ||

225. | Numerical Integration of Izhikevich and HH model neurons (Stewart and Bair 2009) | |

The Parker-Sochacki method is a new technique for the numerical integration of differential equations applicable to many neuronal models. Using this method, the solution order can be adapted according to the local conditions at each time step, enabling adaptive error control without changing the integration timestep. We apply the Parker-Sochacki method to the Izhikevich ‘simple’ model and a Hodgkin-Huxley type neuron, comparing the results with those obtained using the Runge-Kutta and Bulirsch-Stoer methods. | ||

226. | Odor supported place cell model and goal navigation in rodents (Kulvicius et al. 2008) | |

" ... Here we model odor supported place cells by using a simple feed-forward network and analyze the impact of olfactory cues on place cell formation and spatial navigation. The obtained place cells are used to solve a goal navigation task by a novel mechanism based on self-marking by odor patches combined with a Q-learning algorithm. We also analyze the impact of place cell remapping on goal directed behavior when switching between two environments. ..." | ||

227. | Olfactory bulb juxtaglomerular models (Carey et al., 2015) | |

" ...We investigated how OB circuits shape inhalation-driven dynamics in MCs using a modeling approach that was highly constrained by experimental results. First, we constructed models of canonical OB circuits that included mono- and disynaptic feedforward excitation, recurrent inhibition and feedforward inhibition of the MC. We then used experimental data to drive inputs to the models and to tune parameters; inputs were derived from sensory neuron responses during natural odorant sampling (sniffing) in awake rats, and model output was compared to recordings of MC responses to odorants sampled with the same sniff waveforms. This approach allowed us to identify OB circuit features underlying the temporal transformation of sensory inputs into inhalation-linked patterns of MC spike output. ..." | ||

228. | Olfactory Bulb mitral-granule network generates beta oscillations (Osinski & Kay 2016) | |

This model of the dendrodendritic mitral-granule synaptic network generates gamma and beta oscillations as a function of the granule cell excitability, which is represented by the granule cell resting membrane potential. | ||

229. | Olfactory bulb network: neurogenetic restructuring and odor decorrelation (Chow et al. 2012) | |

Adult neurogenesis in the olfactory bulb has been shown experimentally to contribute to perceptual learning. Using a computational network model we show that fundamental aspects of the adult neurogenesis observed in the olfactory bulb -- the persistent addition of new inhibitory granule cells to the network, their activity-dependent survival, and the reciprocal character of their synapses with the principal mitral cells -- are sufficient to restructure the network and to alter its encoding of odor stimuli adaptively so as to reduce the correlations between the bulbar representations of similar stimuli. The model captures the experimentally observed role of neurogenesis in perceptual learning and the enhanced response of young granule cells to novel stimuli. Moreover, it makes specific predictions for the type of odor enrichment that should be effective in enhancing the ability of animals to discriminate similar odor mixtures. NSF grant DMS-0719944. | ||

230. | Olfactory receptor neuron model (Dougherty et al 2005) | |

Demonstration of ORN model by Dougherty, Wright and Yew (2005) PNAS 102: 10415-10420. This program, dwy_pnas_demo2, simulates the transduction current response of a single olfactory receptor neuron being stimulated by an odorant plume. The program is interactive in that a user can tweak parameter values and stimulus conditions. Also, users can save a configuration in a mat-file or export all aspects to a directory of text files. These text files can be read by other programs. There is also an import facility for importing text files from a directory that allows the user to specify their own data, pulses and parameters. | ||

231. | Opponent-channel model of the cortical representation of auditory space (Briley et al., 2012) | |

This is the computational opponent-channel model used by Briley et al. (2012) to model electroencephalographic (EEG) responses from human auditory cortex to abrupt shifts in sound-source location and to predict psychophysical measures of spatial acuity. The zip file contains both a Matlab and an Excel implementation of the model. Details of use are contained within each file. | ||

232. | Optimal Localist and Distributed Coding Through STDP (Masquelier & Kheradpisheh 2018) | |

We show how a LIF neuron equipped with STDP can become optimally selective, in an unsupervised manner, to one or several repeating spike patterns, even when those patterns are hidden in Poisson spike trains. | ||

233. | Optimal spatiotemporal spike pattern detection by STDP (Masquelier 2017) | |

We simulate a LIF neuron equipped with STDP. A pattern repeats in its inputs. The LIF progressively becomes selective to the repeating pattern, in an optimal manner. | ||

234. | Origin of heterogeneous spiking patterns in spinal dorsal horn neurons (Balachandar & Prescott 2018) | |

"Neurons are often classified by spiking pattern. Yet, some neurons exhibit distinct patterns under subtly different test conditions, which suggests that they operate near an abrupt transition, or bifurcation. A set of such neurons may exhibit heterogeneous spiking patterns not because of qualitative differences in which ion channels they express, but rather because quantitative differences in expression levels cause neurons to operate on opposite sides of a bifurcation. Neurons in the spinal dorsal horn, for example, respond to somatic current injection with patterns that include tonic, single, gap, delayed and reluctant spiking. It is unclear whether these patterns reflect five cell populations (defined by distinct ion channel expression patterns), heterogeneity within a single population, or some combination thereof. We reproduced all five spiking patterns in a computational model by varying the densities of a low-threshold (KV1-type) potassium conductance and an inactivating (A-type) potassium conductance and found that single, gap, delayed and reluctant spiking arise when the joint probability distribution of those channel densities spans two intersecting bifurcations that divide the parameter space into quadrants, each associated with a different spiking pattern. ... " | ||

235. | Oscillating neurons in the cochlear nucleus (Bahmer Langner 2006a, b, and 2007) | |

"Based on the physiological and anatomical data, we propose a model consisting of a minimum network of two choppers that are interconnected with a synaptic delay of 0.4 ms (Bahmer and Langner 2006a) . Such minimum delays have been found in different systems and in various animals (e.g. Hackett, Jackson, and Rubel 1982; Borst, Helmchen, and Sakmann 1995). The choppers receive input from both the auditory nerve and an onset neuron. This model can reproduce the mean, standard deviation, and coefficient of variation of the ISI and the dynamic features of AM coding of choppers." | ||

236. | Oscillation and coding in a proposed NN model of insect olfaction (Horcholle-Bossavit et al. 2007) | |

"For the analysis of coding mechanisms in the insect olfactory system, a fully connected network of synchronously updated McCulloch and Pitts neurons (MC-P type) was (previously) developed. ... Considering the update time as an intrinsic clock, this “Dynamic Neural Filter” (DNF), which maps regions of input space into spatio-temporal sequences of neuronal activity, is able to produce exact binary codes extracted from the synchronized activities recorded at the level of projection neurons (PN) in the locust antennal lobe (AL) in response to different odors ... We find synaptic matrices which lead to both the emergence of robust oscillations and spatio-temporal patterns, using a formal criterion, based on a Normalized Euclidian Distance (NED), in order to measure the use of the temporal dimension as a coding dimension by the DNF. Similarly to biological PN, the activity of excitatory neurons in the model can be both phase-locked to different cycles of oscillations which (is reminiscent of the) local field potential (LFP), and nevertheless exhibit dynamic behavior complex enough to be the basis of spatio-temporal codes." | ||

237. | Oscillations emerging from noise-driven NNs (Tchumatchenko & Clopath 2014) | |

" ... Here we show how the oscillation frequency is shaped by single neuron resonance, electrical and chemical synapses.The presence of both gap junctions and subthreshold resonance are necessary for the emergence of oscillations. Our results are in agreement with several experimental observations such as network responses to oscillatory inputs and offer a much-needed conceptual link connecting a collection of disparate effects observed in networks." | ||

238. | Oversampling method to extract excitatory and inhibitory conductances (Bedard et al. 2012) | |

" ... We present here a new method that allows extracting estimates of the full time course of excitatory and inhibitory conductances from single-trial Vm recordings. This method is based on oversampling of the Vm . We test the method numerically using models of increasing complexity. Finally, the method is evaluated using controlled conductance injection in cortical neurons in vitro using the dynamic-clamp technique. ..." | ||

239. | Pancreatic Beta Cell signalling pathways (Fridlyand & Philipson 2016) (MATLAB) | |

This is a 3rd party implementation of Fridlyand & Philipson 2016 who's abstract begins "Insulin secretory in pancreatic beta-cells responses to nutrient stimuli and hormonal modulators include multiple messengers and signaling pathways with complex interdependencies. Here we present a computational model that incorporates recent data on glucose metabolism, plasma membrane potential, G-protein-coupled-receptors (GPCR), cytoplasmic and endoplasmic reticulum calcium dynamics, cAMP and phospholipase C pathways that regulate interactions between second messengers in pancreatic beta-cells. The values of key model parameters were inferred from published experimental data. The model gives a reasonable fit to important aspects of experimentally measured metabolic and second messenger concentrations and provides a framework for analyzing the role of metabolic, hormones and neurotransmitters changes on insulin secretion. Our analysis of the dynamic data provides support for the hypothesis that activation of Ca2+-dependent adenylyl cyclases play a critical role in modulating the effects of glucagon-like peptide 1 (GLP-1), glucose-dependent insulinotropic polypeptide (GIP) and catecholamines. ..." | ||

240. | Parallel cortical inhibition processing enables context-dependent behavior (Kuchibhotla et al. 2016) | |

Physical features of sensory stimuli are fixed, but sensory perception is context dependent. The precise mechanisms that govern contextual modulation remain unknown. Here, we trained mice to switch between two contexts: passively listening to pure tones and performing a recognition task for the same stimuli. Two-photon imaging showed that many excitatory neurons in auditory cortex were suppressed during behavior, while some cells became more active. Whole-cell recordings showed that excitatory inputs were affected only modestly by context, but inhibition was more sensitive, with PV+, SOM+, and VIP+ interneurons balancing inhibition and disinhibition within the network. Cholinergic modulation was involved in context switching, with cholinergic axons increasing activity during behavior and directly depolarizing inhibitory cells. Network modeling captured these findings, but only when modulation coincidently drove all three interneuron subtypes, ruling out either inhibition or disinhibition alone as sole mechanism for active engagement. Parallel processing of cholinergic modulation by cortical interneurons therefore enables context-dependent behavior. | ||

241. | Parameter estimation for Hodgkin-Huxley based models of cortical neurons (Lepora et al. 2011) | |

Simulation and fitting of two-compartment (active soma, passive dendrite) for different classes of cortical neurons. The fitting technique indirectly matches neuronal currents derived from somatic membrane potential data rather than fitting the voltage traces directly. The method uses an analytic solution for the somatic ion channel maximal conductances given approximate models of the channel kinetics, membrane dynamics and dendrite. This approach is tested on model-derived data for various cortical neurons. | ||

242. | Peripheral nerve:Morris-Lecar implementation of (Schwarz et al 1995) | |

This is a Morris-Lecar version of the model in Schwarz et al 1995. The original model in the paper was implemented in the Hodgkin-Huxley style. | ||

243. | Phase oscillator models for lamprey central pattern generators (Varkonyi et al. 2008) | |

In our paper, Varkonyi et al. 2008, we derive phase oscillator models for the lamprey central pattern generator from two biophysically based segmental models. We study intersegmental coordination and show how these models can provide stable intersegmental phase lags observed in real animals. | ||

244. | Phase plane reveals two slow variables in midbrain dopamine neuron bursts (Yu and Canavier, 2015) | |

"Midbrain dopamine neurons exhibit a novel type of bursting that we call “inverted square wave bursting” when exposed to Ca2+-activated small conductance (SK) K+ channel blockers in vitro. This type of bursting has three phases: hyperpolarized silence, spiking, and depolarization block. We find that two slow variables are required for this type of bursting, and we show that the three-dimensional bifurcation diagram for inverted square wave bursting is a folded surface with upper (depolarized) and lower (hyperpolarized) branches. ..." | ||

245. | Phase precession through acceleration of local theta rhythm (Castro & Aguiar 2011) | |

"... Here we present a biophysical spiking model for phase precession in hippocampal CA1 which focuses on the interaction between place cells and local inhibitory interneurons. The model’s functional block is composed of a place cell (PC) connected with a local inhibitory cell (IC) which is modulated by the population theta rhythm. Both cells receive excitatory inputs from the entorhinal cortex (EC). ..." | ||

246. | Phase response curves firing rate dependency of rat purkinje neurons in vitro (Couto et al 2015) | |

NEURON implementation of stochastic gating in the Khaliq-Raman Purkinje cell model. NEURON implementation of the De Schutter and Bower model of a Purkinje Cell. Matlab scripts to compute the Phase Response Curve (PRC). LCG configuration files to experimentally determine the PRC. Integrate and Fire models (leaky and non-leaky) implemented in BRIAN to see the influence of the PRC in a network of unconnected neurons receiving sparse common input. | ||

247. | Phasic ACh promotes gamma oscillations in E-I networks (Lu et al, 2020) | |

In a biophysically-based model, we show that a network of excitatory (E) and inhibitory (I) neurons that initially displays asynchronous firing can generate transient gamma oscillatory activity in response to simulated brief pulses of ACh. ACh effects are simulated as transient modulation of the conductance of an M-type K+ current which is blocked by activation of muscarinic receptors and has significant effects on neuronal excitability. The ACh-induced effects on the M current conductance, gks, change network dynamics to promote the emergence of network gamma rhythmicity through a Pyramidal-Interneuronal Network Gamma (PING) mechanism. | ||

248. | Pipette and membrane patch geometry effects on GABAa currents patch-clamp exps (Moroni et al. 2011) | |

Ion currents, mediated by GABAa-receptors in outside-out membrane patches, may alter the concentration of Chloride ions inside the pipette and the membrane patch. GABAa-receptors are in fact ionotropic synaptic receptors, selective to Chloride ions. Therefore, chloride fluxes across the membrane patch correlate to GABAa-receptor opening. Chloride ions accumulation, depletion and diffusion, inside the pipette and the membrane patch, affect by definition the Chloride equilibrium (i.e. Nernst) electrical potential. This in turn changes the ionic driving force underlying GABAa-mediated currents. It follows that, in case of very small volumes and confined geometries, voltage-clamp recordings of GABAa-receptor currents carry information on both i) Chloride diffusion and ii) receptor kinetics. The relevance of (i) and (ii) have been studied numerically by defining a 1-dimensional biophysical model, released here to the interested user. | ||

249. | Plasticity forms non-overlapping adjacent ON and OFF RFs in cortical neurons (Sollini et al 2018) | |

Hebbian plasticity of a feedforward network modelling ON-OFF receptive field changes in auditory cortex. | ||

250. | Pleiotropic effects of SCZ-associated genes (Mäki-Marttunen et al. 2017) | |

Python and MATLAB scripts for studying the dual effects of SCZ-related genes on layer 5 pyramidal cell firing and sinoatrial node cell pacemaking properties. The study is based on two L5PC models (Hay et al. 2011, Almog & Korngreen 2014) and SANC models (Kharche et al. 2011, Severi et al. 2012). | ||

251. | Point process framework for modeling electrical stimulation of auditory nerve (Goldwyn et al. 2012) | |

A point process model of the auditory nerve that provides a compact and accurate description of neural responses to electric stimulation. Inspired by the framework of generalized linear models, the model consists of a cascade of linear and nonlinear stages. A semi-analytical procedure uniquely determines each parameter in the model on the basis of fundamental statistics from recordings of single fiber responses to electric stimulation, including threshold, relative spread, jitter, and chronaxie. The model also accounts for refractory and summation effects that influence the responses of auditory nerve fibers to high pulse rate stimulation. | ||

252. | Population-level model of the basal ganglia and action selection (Gurney et al 2001, 2004) | |

We proposed a new functional architecture for the basal ganglia (BG) based on the premise that these brain structures play a central role in behavioural action selection. The papers quantitatively describes the properties of the model using analysis and simulation. In the first paper, we show that the decomposition of the BG into selection and control pathways is supported in several ways. First, several elegant features are exposed--capacity scaling, enhanced selectivity and synergistic dopamine modulation--which might be expected to exist in a well designed action selection mechanism. Second, good matches between model GPe output and GPi and SNr output, and neurophysiological data, have been found. Third, the behaviour of the model as a signal selection mechanism has parallels with some kinds of action selection observed in animals under various levels of dopaminergic modulation. In the second paper, we extend the BG model to include new connections, and show that action selection is maintained. In addition, we provide quantitative measures for defining different forms of selection, and methods for assessing performance changes in computational neuroscience models. | ||

253. | Prob. Inference of Short-Term Synaptic Plasticity in Neocort. Microcircuits (Costa et al. 2013) | |

" ... As a solution (for Short Term Plasticity (STP) inference), we introduce a Bayesian formulation, which yields the posterior distribution over the model parameters given the data. First, we show that common STP protocols yield broad distributions over some model parameters. Using our result we propose a experimental protocol to more accurately determine synaptic dynamics parameters. Next, we infer the model parameters using experimental data from three different neocortical excitatory connection types. This reveals connection-specific distributions, which we use to classify synaptic dynamics. Our approach to demarcate connection-specific synaptic dynamics is an important improvement on the state of the art and reveals novel features from existing data." | ||

254. | Purkinje neuron network (Zang et al. 2020) | |

Both spike rate and timing can transmit information in the brain. Phase response curves (PRCs) quantify how a neuron transforms input to output by spike timing. PRCs exhibit strong firing-rate adaptation, but its mechanism and relevance for network output are poorly understood. Using our Purkinje cell (PC) model we demonstrate that the rate adaptation is caused by rate-dependent subthreshold membrane potentials efficiently regulating the activation of Na+ channels. Then we use a realistic PC network model to examine how rate-dependent responses synchronize spikes in the scenario of reciprocal inhibition-caused high-frequency oscillations. The changes in PRC cause oscillations and spike correlations only at high firing rates. The causal role of the PRC is confirmed using a simpler coupled oscillator network model. This mechanism enables transient oscillations between fast-spiking neurons that thereby form PC assemblies. Our work demonstrates that rate adaptation of PRCs can spatio-temporally organize the PC input to cerebellar nuclei. | ||

255. | Pyramidal neurons with mutated SCN2A gene (Nav1.2) (Ben-Shalom et al 2017) | |

Model of pyramidal neurons that either hyper or hypo excitable due to SCN2A mutations. Mutations are taken from patients with ASD or Epilepsy | ||

256. | Pyramidal neurons: IKHT offsets activation of IKLT to increase gain (Fernandez et al 2005) | |

This matlab model was supplied by Dr Fernandez. It provides the model specification for the below paper. The influence of a high threshold K current on low threshold K and Na currents (especially frequency-current relationships) are studied in the paper with both experiments and modeling. Please see the reference for more and details. | ||

257. | QIF method to estimate synaptic conductances (Vich et al 2017) | |

"Subthreshold fluctuations in neuronal membrane potential traces contain nonlinear components, and employing nonlinear models might improve the statistical inference. We propose a new strategy to estimate synaptic conductances, which has been tested using in silico data and applied to in vivo recordings. The model is constructed to capture the nonlinearities caused by subthreshold activated currents, and the estimation procedure can discern between excitatory and inhibitory conductances using only one membrane potential trace. ... The results show an improvement compared to existent procedures for the models tested here." | ||

258. | Reaching movements with robust or stochastic optimal control models (Crevecoeur et al 2019) | |

"We explored the hypothesis that compensation for unmodelled disturbances was supported by a robust neural control strategy. We studied the predictions of stochastic optimal control (LQG) (Linear Quadratic Gaussian) (Todorov, 2005) and a robust control design that can equivalently be described as a “min-max” or worst-case strategy (Basar and Bernhard, 1991) applied to linear models of planar reaching movements. The robust controller displayed an increase in control gains, resulting in faster movements towards the target and more vigorous responses to perturbations. Our experimental results supported these predictions: the occurrence of unexpected force field disturbances evoked both faster movements and more vigorous responses to perturbations. Thus, the neural controller was more robust in the sense that the feedback responses reduced the impact of the perturbations (step and force field). Thus the compensation for disturbances involved a “model-free” component. ..." | ||

259. | Reconstrucing sleep dynamics with data assimilation (Sedigh-Sarvestani et al., 2012) | |

We have developed a framework, based on the unscented Kalman filter, for estimating hidden states and parameters of a network model of sleep. The network model includes firing rates and neurotransmitter output of 5 cell-groups in the rat brain. | ||

260. | Reconstructing cerebellar granule layer evoked LFP using convolution (ReConv) (Diwakar et al. 2011) | |

The model allows reconstruction of evoked local field potentials as seen in the cerebellar granular layer. The approach uses a detailed model of cerebellar granule neuron to generate data traces and then uses a "ReConv" or jittered repetitive convolution technique to reproduce post-synaptic local field potentials in the granular layer. The algorithm was used to generate both in vitro and in vivo evoked LFP and reflected the changes seen during LTP and LTD, when such changes were induced in the underlying neurons by modulating release probability of synapses and sodium channel regulated intrinsic excitability of the cells. | ||

261. | Reflected SDE Hodgkin-Huxley Model (Dangerfield et al. 2012) | |

Matlab code for simulating channel noise using the original Hodgkin-Huxley equations and a variant of the Hodkgin-Huxley model from (Bruce, Annals Bio Eng, Vol 36, pp 824-838, 2009). Methods used in simulation are SSA, SDE method and RSDE method. | ||

262. | Reichardt Model for Motion Detection in the Fly Visual System (Tuthill et al, 2011) | |

This simulation implements a correlation-type model for visual motion detection, as originally described by Hassenstein and Reichardt (1956), and analyzes the response of the model to standard and reverse-phi motion stimuli. Details are provided in: Tuthill JC, et al. (2011) | ||

263. | Reinforcement Learning with Forgetting: Linking Sustained Dopamine to Motivation (Kato Morita 2016) | |

"It has been suggested that dopamine (DA) represents reward-prediction-error (RPE) defined in reinforcement learning and therefore DA responds to unpredicted but not predicted reward. However, recent studies have found DA response sustained towards predictable reward in tasks involving self-paced behavior, and suggested that this response represents a motivational signal. We have previously shown that RPE can sustain if there is decay/forgetting of learned-values, which can be implemented as decay of synaptic strengths storing learned-values. This account, however, did not explain the suggested link between tonic/sustained DA and motivation. In the present work, we explored the motivational effects of the value-decay in self-paced approach behavior, modeled as a series of ‘Go’ or ‘No-Go’ selections towards a goal. Through simulations, we found that the value-decay can enhance motivation, specifically, facilitate fast goal-reaching, albeit counterintuitively. ..." | ||

264. | Relative spike time coding and STDP-based orientation selectivity in V1 (Masquelier 2012) | |

Phenomenological spiking model of the cat early visual system. We show how natural vision can drive spike time correlations on sufficiently fast time scales to lead to the acquisition of orientation-selective V1 neurons through STDP. This is possible without reference times such as stimulus onsets, or saccade landing times. But even when such reference times are available, we demonstrate that the relative spike times encode the images more robustly than the absolute ones. | ||

265. | Reliability of Morris-Lecar neurons with added T, h, and AHP currents (Zeldenrust et al. 2013) | |

We investigated the reliability of the timing of spikes in a spike train in a Morris-Lecar model with several extensions. A frozen Gaussian noise current, superimposed on a DC current, was injected. The neuron responded with spike trains that showed trial-to-trial variability. The reliability depends on the shape (steepness) of the current input versus spike frequency output curve. The model also allowed to study the contribution of three relevant ionic membrane currents to reliability: a T-type calcium current, a cation selective h-current and a calcium dependent potassium current in order to allow bursting, investigate the consequences of a more complex current-frequency relation and produce realistic firing rates. | ||

266. | Respiratory central pattern generator including Kolliker-Fuse nucleus (Wittman et al 2019) | |

We present three highly reduced conductance-based models for the core of the respiratory CPG. All successfully simulate respiratory outputs across eupnoeic and vagotomized conditions and show that loss of inhibition to the pontine Kolliker-Fuse nucleus reproduces the key respiratory alterations associated with Rett syndrome. | ||

267. | Respiratory control model with brainstem CPG and sensory feedback (Diekman, Thomas, and Wilson 2017) | |

This is a closed-loop respiratory control model incorporating a central pattern generator (CPG), the Butera-Rinzel-Smith (BRS) model, together with lung mechanics, oxygen handling, and chemosensory components. The closed-loop system exhibits bistability of bursting and tonic spiking. Bursting corresponds to coexistence of eupnea-like breathing, with normal minute ventilation and blood oxygen level. Tonic spiking corresponds to a tachypnea-like state, with pathologically reduced minute ventilation and critically low blood oxygen. In our paper, we use the closed-loop system to demonstrate robustness to changes in metabolic demand, spontaneous autoresuscitation in response to hypoxia, and the distinct mechanisms that underlie rhythmogenesis in the intact control circuit vs. the isolated, open-loop CPG. | ||

268. | Response properties of neocort. neurons to temporally modulated noisy inputs (Koendgen et al. 2008) | |

Neocortical neurons are classified by current–frequency relationship. This is a static description and it may be inadequate to interpret neuronal responses to time-varying stimuli. Theoretical studies (Brunel et al., 2001; Fourcaud-Trocmé et al. 2003; Fourcaud-Trocmé and Brunel 2005; Naundorf et al. 2005) suggested that single-cell dynamical response properties are necessary to interpret ensemble responses to fast input transients. Further, it was shown that input-noise linearizes and boosts the response bandwidth, and that the interplay between the barrage of noisy synaptic currents and the spike-initiation mechanisms determine the dynamical properties of the firing rate. In order to allow a reader to explore such simulations, we prepared a simple NEURON implementation of the experiments performed in Köndgen et al., 2008 (see also Fourcaud-Trocmé al. 2003; Fourcaud-Trocmé and Brunel 2005). In addition, we provide sample MATLAB routines for exploring the sandwich model proposed in Köndgen et al., 2008, employing a simple frequdency-domain filtering. The simulations and the MATLAB routines are based on the linear response properties of layer 5 pyramidal cells estimated by injecting a superposition of a small-amplitude sinusoidal wave and a background noise, as in Köndgen et al., 2008. | ||

269. | Resurgent Na+ current offers noise modulation in bursting neurons (Venugopal et al 2019) | |

"Neurons utilize bursts of action potentials as an efficient and reliable way to encode information. It is likely that the intrinsic membrane properties of neurons involved in burst generation may also participate in preserving its temporal features. Here we examined the contribution of the persistent and resurgent components of voltage-gated Na+ currents in modulating the burst discharge in sensory neurons. Using mathematical modeling, theory and dynamic-clamp electrophysiology, we show that, distinct from the persistent Na+ component which is important for membrane resonance and burst generation, the resurgent Na+ can help stabilize burst timing features including the duration and intervals. ..." | ||

270. | Reverse-time correlation analysis for idealized orientation tuning dynamics (Kovacic et al. 2008) | |

"A theoretical analysis is presented of a reverse-time correlation method used in experimentally investigating orientation tuning dynamics of neurons in the primary visual cortex. An exact mathematical characterization of the method is developed, and its connection with the Volterra–Wiener nonlinear systems theory is described. Various mathematical consequences and possible physiological implications of this analysis are illustrated using exactly solvable idealized models of orientation tuning." | ||

271. | Revised opponent-channel model of auditory space cortical representation (Briley & Summerfield 2013) | |

This is the computational opponent-channel model used by Briley et al. (2013) to model electroencephalographic (EEG) responses from the auditory cortices of young, younger-old and older-old adults to abrupt shifts in sound-source location, and to predict each groups' psychophysical measures of spatial acuity. | ||

272. | Self-influencing synaptic plasticity (Tamosiunaite et al. 2007) | |

"... Similar to a previous study (Saudargiene et al., 2004) we employ a differential Hebbian learning rule to emulate spike-timing dependent plasticity and investigate how the interaction of dendritic and back-propagating spikes, as the post-synaptic signals, could influence plasticity. ..." | ||

273. | Sequential neuromodulation of Hebbian plasticity in reward-based navigation (Brzosko et al 2017) | |

" ...Here, we demonstrate that sequential neuromodulation of STDP by acetylcholine and dopamine offers an efficacious model of reward-based navigation. Specifically, our experimental data in mouse hippocampal slices show that acetylcholine biases STDP toward synaptic depression, whilst subsequent application of dopamine converts this depression into potentiation. Incorporating this bidirectional neuromodulation-enabled correlational synaptic learning rule into a computational model yields effective navigation toward changing reward locations, as in natural foraging behavior. ..." | ||

274. | SHOT-CA3, RO-CA1 Training, & Simulation CODE in models of hippocampal replay (Nicola & Clopath 2019) | |

In this code, we model the interaction between the medial septum and hippocampus as a FORCE trained, dual oscillator model. One oscillator corresponds to the medial septum and serves as an input, while a FORCE trained network of LIF neurons acts as a model of the CA3. We refer to this entire model as the Septal Hippocampal Oscillator Theta (or SHOT) network. The code contained in this upload allows a user to train a SHOT network, train a population of reversion interneurons, and simulate the SHOT-CA3 and RO-CA1 networks after training. The code scripts are labeled to correspond to the figure from the manuscript. | ||

275. | Simple and accurate Diffusion Approximation algor. for stochastic ion channels (Orio & Soudry 2012) | |

" ... We derived the (Stochastic Differential Equations) SDE explicitly for any given ion channel kinetic scheme. The resulting generic equations were surprisingly simple and interpretable – allowing an easy, transparent and efficient (Diffusion Approximation) DA implementation, avoiding unnecessary approximations. The algorithm was tested in a voltage clamp simulation and in two different current clamp simulations, yielding the same results as (Markov Chains) MC modeling. Also, the simulation efficiency of this DA method demonstrated considerable superiority over MC methods, except when short time steps or low channel numbers were used." | ||

276. | Simulating ion channel noise in an auditory brainstem neuron model (Schmerl & McDonnell 2013) | |

" ... Here we demonstrate that biophysical models of channel noise can give rise to two kinds of recently discovered stochastic facilitation effects in a Hodgkin-Huxley-like model of auditory brainstem neurons. The first, known as slope-based stochastic resonance (SBSR), enables phasic neurons to emit action potentials that can encode the slope of inputs that vary slowly relative to key time constants in the model. The second, known as inverse stochastic resonance (ISR), occurs in tonically firing neurons when small levels of noise inhibit tonic firing and replace it with burstlike dynamics. ..." Preprint available at http://arxiv.org/abs/1311.2643 | ||

277. | Single E-I oscillating network with amplitude modulation (Avella Gonzalez et al. 2012) | |

"... Intriguingly, the amplitude of ongoing oscillations, such as measured in EEG recordings, fluctuates irregularly, with episodes of high amplitude (HAE) alternating with episodes of low amplitude (LAE). ... Here, we show that transitions between HAE and LAE in the alpha/beta frequency band occur in a generic neuronal network model consisting of interconnected inhibitory (I) and excitatory (E) cells that are externally driven by sustained depolarizing currents(cholinergic input) and trains of action potentials that activate excitatory synapses. In the model, action potentials onto inhibitory cells represent input from other brain areas and desynchronize network activity, being crucial for the emergence of amplitude fluctuations. ..." | ||

278. | Single neuron with ion concentrations to model anoxic depolarization (Zandt et al. 2011) | |

A minimal single neuron model, including changing ion concentrations and homeostasis mechanisms. It shows the sudden depolarization that occurs after prolonged anoxia/ischemia. | ||

279. | Slow wave propagation in the guinea-pig gastric antrum (Hirst et al. 2006, Edwards and Hirst 2006) | |

"(Edwards and Hirst 2006) provides an electrical description of the propagation of slow waves and pacemaker potentials in the guinea-pig gastric antrum in anal and circumferential directions. As electrical conduction between laterally adjacent circular muscle bundles is regularly interrupted, anal conduction of pacemaker potentials was assumed to occur via an electrically interconnected chain of myenteric interstitial cells of Cajal (ICCMY). ICCMY were also connected resistively to serially connected compartments of longitudinal muscle. Circumferential conduction occurred in a circular smooth muscle bundle that was represented as a chain of electrically connected isopotential compartments: each compartment contained a proportion of intramuscular interstitial cells of Cajal (ICCIM) that are responsible for the regenerative component of the slow wave. The circular muscle layer, which contains ICCIM, and the ICCMY network incorporated a mechanism, modelled as a two-stage chemical reaction, which produces an intracellular messenger. ... The model generates pacemaker potentials and slow waves with propagation velocities similar to those determined in the physiological experiments described in the accompanying paper." | ||

280. | Smoothing of, and parameter estimation from, noisy biophysical recordings (Huys & Paninski 2009) | |

" ... Sequential Monte Carlo (“particle filtering”) methods, in combination with a detailed biophysical description of a cell, are used for principled, model-based smoothing of noisy recording data. We also provide an alternative formulation of smoothing where the neural nonlinearities are estimated in a non-parametric manner. Biophysically important parameters of detailed models (such as channel densities, intercompartmental conductances, input resistances, and observation noise) are inferred automatically from noisy data via expectation-maximisation. ..." | ||

281. | Sound-evoked activity in peripheral axons of type I spiral ganglion neurons (Budak et al. 2021) | |

Using this model, we investigated the implications of two mechanisms underlying the auditory neuropathy known as hidden hearing loss, namely synaptopathy and myelinopathy, on sound-evoked spike generation and timing in the peripheral axons of type I spiral ganglion neurons (SGNs). The model is a reduced biophysical model consisting of a population of myelinated SGN axonal fibers whose firing activity is driven by a previously developed, well accepted model for cochlear sound processing. Using the model, we investigated how synapse loss (synaptopathy) or disruption of myelin organization (myelinopathy) affected spike generation on the axons and the profile of the compound action potential (CAP) signal computed from the spike activity. Synaptopathy and myelinopathy were implemented by removing synapses and by varying the position of SGN heminodes (the nodal structures closest to the inner hair cell synapse where action potentials are generated), respectively. Model results showed that heminode disruption caused decreased amplitude and increased latency of sound-evoked CAPs. In addition, significant elongation of the initial axon segment caused spike generation failure leading to decreased spiking probability. In contrast, synaptopathy, solely decreased probability of firing, subsequently decreasing CAP peak amplitude without affecting its latency, similar to observations in noise exposed animals. Model results reveal the disruptive effect of synaptopathy or myelinopathy on neural activity in the peripheral auditory system that may contribute to perceptual deficits. | ||

282. | Sparse connectivity is required for decorrelation, pattern separation (Cayco-Gajic et al 2017) | |

" ... To investigate the structural and functional determinants of pattern separation we built models of the cerebellar input layer with spatially correlated input patterns, and systematically varied their synaptic connectivity. ..." | ||

283. | Spatially-varying glutamate diffusion coefficient at CA1 synaptic cleft space (Gupta et al. 2016) | |

Due to the heterogeneous macromolecular crowding and geometrical irregularity at central excitatory synapses, the diffusion coefficient of glutamate may exhibit spatial variation across the cleft space. To take into account the effect of emergent cleft heterogeneity on the generation of excitatory postsynaptic currents (EPSCs), a gamma statistical distribution of the glutamate diffusion coefficient is considered and, using the principle of superstatistics, the glutamate transients are computed as well as the activation of AMPA receptors is performed. This model demonstrates the numerical simulation of the Brownian diffusion of glutamate under distributed diffusion coefficient, the subsequent stochastic activation of AMPA receptors using Milstein-Nicoll scheme and modified Gillespie algorithm with minimum time-step correction, and the eventual stochastic profile of EPSC generation. The study is based on the CA1 synapses located at the dendrites of CA1 pyramidal neurons in the mammalian hippocampal region. | ||

284. | Spectral method and high-order finite differences for nonlinear cable (Omurtag and Lytton 2010) | |

We use high-order approximation schemes for the space derivatives in the nonlinear cable equation and investigate the behavior of numerical solution errors by using exact solutions, where available, and grid convergence. The space derivatives are numerically approximated by means of differentiation matrices. A flexible form for the injected current is used that can be adjusted smoothly from a very broad to a narrow peak, which leads, for the passive cable, to a simple, exact solution. We provide comparisons with exact solutions in an unbranched passive cable, the convergence of solutions with progressive refinement of the grid in an active cable, and the simulation of spike initiation in a biophysically realistic single-neuron model. | ||

285. | Spike burst-pause dynamics of Purkinje cells regulate sensorimotor adaptation (Luque et al 2019) | |

"Cerebellar Purkinje cells mediate accurate eye movement coordination. However, it remains unclear how oculomotor adaptation depends on the interplay between the characteristic Purkinje cell response patterns, namely tonic, bursting, and spike pauses. Here, a spiking cerebellar model assesses the role of Purkinje cell firing patterns in vestibular ocular reflex (VOR) adaptation. The model captures the cerebellar microcircuit properties and it incorporates spike-based synaptic plasticity at multiple cerebellar sites. ..." | ||

286. | Spike frequency adaptation in the LGMD (Peron and Gabbiani 2009) | |

This model is used in the referenced paper to demonstrate that a model of an SK-like calcium-sensitive potassium (KCa) conductance can replicate the spike frequency adaptation (SFA) of the locust lobula giant movement detector (LGMD) neuron. The model simulates current injection experiments with and without KCa block in the LGMD, as well as visual stimulation experiments with and without KCa block. | ||

287. | Spike Response Model simulator (Jolivet et al. 2004, 2006, 2008) | |

The Spike Response Model (SRM) optimized on the experimental data in the Single-Neuron modelling Competition ( www.incf.org/community/competitions ) for edition 2007 and edition 2008. The Spike Response Model is a simplified model of neuronal excitability where current linearly integrates to an artificial threshold. After the spike, the threshold is augmented and the voltage follows a voltage kernel that is the average voltage trace during and after a spike. The parameters were chosen to best fit the observed spike times with a method outlined in Jolivet et al. (2006). | ||

288. | Spike timing detection in different forms of LTD (Doi et al 2005) | |

To understand the spike-timing detection mechanisms in cerebellar long-term depression (LTD), we developed a kinetic model of Ca dynamics within a Purkinje dendritic spine. In our kinetic simulation, IP3 was first produced via the metabotropic pathway of parallel fiber (PF) inputs, and the Ca influx in response to the climbing fiber (CF) input triggered regenerative Ca-induced Ca release from the internal stores via the IP3 receptors activated by the increased IP3. The delay in IP3 increase caused by the PF metabotropic pathway generated the optimal PF–CF interval. The Ca dynamics revealed a threshold for large Ca2 release that decreased as IP3 increased, and it coherently explained the different forms of LTD. See paper for more and details. | ||

289. | Spiking GridPlaceMap model (Pilly & Grossberg, PLoS One, 2013) | |

Development of spiking grid cells and place cells in the entorhinal-hippocampal system to represent positions in large spaces | ||

290. | Spiking neuron model of the basal ganglia (Humphries et al 2006) | |

A spiking neuron model of the basal ganglia (BG) circuit (striatum, STN, GP, SNr). Includes: parallel anatomical channels; tonic dopamine; dopamine receptors in striatum, STN, and GP; burst-firing in STN; GABAa, AMPA, and NMDA currents; effects of synaptic location. Model demonstrates selection and switching of input signals. Replicates experimental data on changes in slow-wave (<1 Hz) and gamma-band oscillations within BG nuclei following lesions and pharmacological manipulations. | ||

291. | Spinal circuits controlling limb coordination and gaits in quadrupeds (Danner et al 2017) | |

Simulation of spinal neural networks involved in the central control of interlimb coordination and speed-dependent gait expression in quadrupeds. | ||

292. | State dependent drug binding to sodium channels in the dentate gyrus (Thomas & Petrou 2013) | |

A Markov model of sodium channels was developed that includes drug binding to fast inactivated states. This was incorporated into a model of the dentate gyrus to investigate the effects of anti-epileptic drugs on neuron and network properties. | ||

293. | Statistical Long-term Synaptic Plasticity (statLTSP) (Costa et al 2017) | |

In this paper we introduce a new statistical view of long-term synaptic plasticity, in which the postsynaptic responses are optimised towards a bound (or target). This in turn explains a wide range of experimental data. | ||

294. | Statistics of symmetry measure for networks of neurons (Esposito et al. 2014) | |

The code reproduces Figures 1, 2, 3A and 3C from Esposito et al "Measuring symmetry, asymmetry and randomness in neural networks". It provides the statistics of the symmetry measure defined in the paper for networks of neurons with random connections drawn from uniform and gaussian distributions. | ||

295. | STDP allows fast rate-modulated coding with Poisson-like spike trains (Gilson et al. 2011) | |

The model demonstrates that a neuron equipped with STDP robustly detects repeating rate patterns among its afferents, from which the spikes are generated on the fly using inhomogenous Poisson sampling, provided those rates have narrow temporal peaks (10-20ms) - a condition met by many experimental Post-Stimulus Time Histograms (PSTH). | ||

296. | Stochastic Hodgkin-Huxley Model: 14x28D Langevin Simulation (Pu and Thomas, 2020). | |

This model provides a natural 14-dimensional Langevin dynamics for the Hodgkin Huxley system in which each directed edge in the ion channel state transition graph acts as an independent noise source, leading to a 14 dimensional state space (1 dimension for voltage, 5 for potassium and 8 for sodium) and 14 × 28 noise coefficient matrix S. In [Pu and Thomas (2020) Neural Computation] we show that this 14 x 28 dimensional model is pathwise equivalent to the 14 x 11 dimensional Langevin model proposed in [Fox and Lu (1994) Phys Rev E], as well as an 14 x 14 model described in [Orio and Soudry (2012) PLoS One]. Unlike Fox and Lu's model, our construction does not require a matrix root extraction step, and runs significantly faster. Unlike Orio and Soudry's model, each directed edge acts as an independent noise source, which facilitates the application of stochastic shielding methods for even greater simulation speed. For comparison, we provide implementations of the following models: 1. Discrete-state Markov chain model (slow, but provides the "gold standard" model), adapted from [Goldwyn and Shea-Brown (2011) PLoS Comp. Biol.] 2. 14 x 11 Langevin model from [Fox and Lu (1994) Phys. Rev. E]. (We implement versions with three different boundary conditions: open boundaries, reflecting boundaries, and resampling/rejection at the boundaries.) 3. 4 x 3 Langevin model from [Fox (1997) Biophys. J.] 4. 14 x 13 Langevin model from [Goldwyn and Shea (2011) PLoS Comp. Biol.] 5. 14 x 14 Langevin model from [Dangerfield et al (2012) Phys. Rev. E] 6. 14 x 14 Langevin model from [Orio and Soudry (2012) PLoS One] 7. 14 x 28 Langevin model from [Pu and Thomas (2020) Neural Computation] implemented both with and without stochastic shielding 8. 14 x 0 deterministic HH model (also from [Pu and Thomas (2020) Neural Computation], with the full 14 dimensional state space but no noise) The Read_me.md file provides more detailed simulations. To cite the code: Pu, Shusen, and Peter J. Thomas. "Fast and Accurate Langevin Simulations of Stochastic Hodgkin-Huxley Dynamics." Neural Computation 32, 1775–1835 (2020) | ||

297. | Stochastic versions of the Hodgkin-Huxley equations (Goldwyn, Shea-Brown 2011) | |

A Matlab gui for simulating different channel noise models using the Hodgkin-Huxley equations. Methods provided and reviewed in Goldwyn and Shea-Brown (2011) are: current noise, subunit noise, conductance noise, and Markov chain, as well as the standard deterministic Hodgkin-Huxley model. | ||

298. | Strategy for kinase transport by microtubules to nerve terminals (Koon et al. 2014) | |

This model was used in the computational study of the strategies of protein transport in the context of JNK (c-JUN NH2-terminal kinase) transport along microtubules to the terminals of neuronal cells. Diffusion governs the first strategy. In the second strategy, proteins of the JNK signaling cascade bind to scaffolds and the whole protein-scaffold cargo is transported by kinesin motors along microtubules. Using the results from the simulations, the two distinct strategies for transport were compared. | ||

299. | Striatal dopamine ramping: an explanation by reinforcement learning with decay (Morita & Kato, 2014) | |

Incorporation of decay of learned values into temporal-difference (TD) learning (Sutton & Barto, 1998, Reinforcement Learning (MIT Press)) causes ramping of TD reward prediction error (RPE), which could explain, given the hypothesis that dopamine represents TD RPE (Montague et al., 1996, J Neurosci 16:1936; Schultz et al., 1997, Science 275:1593), the reported ramping of the dopamine concentration in the striatum in a reward-associated spatial navigation task (Howe et al., 2013, Nature 500:575). | ||

300. | Striatal GABAergic microcircuit, dopamine-modulated cell assemblies (Humphries et al. 2009) | |

To begin identifying potential dynamically-defined computational elements within the striatum, we constructed a new three-dimensional model of the striatal microcircuit's connectivity, and instantiated this with our dopamine-modulated neuron models of the MSNs and FSIs. A new model of gap junctions between the FSIs was introduced and tuned to experimental data. We introduced a novel multiple spike-train analysis method, and apply this to the outputs of the model to find groups of synchronised neurons at multiple time-scales. We found that, with realistic in vivo background input, small assemblies of synchronised MSNs spontaneously appeared, consistent with experimental observations, and that the number of assemblies and the time-scale of synchronisation was strongly dependent on the simulated concentration of dopamine. We also showed that feed-forward inhibition from the FSIs counter-intuitively increases the firing rate of the MSNs. | ||

301. | Striatal GABAergic microcircuit, spatial scales of dynamics (Humphries et al, 2010) | |

The main thrust of this paper was the development of the 3D anatomical network of the striatum's GABAergic microcircuit. We grew dendrite and axon models for the MSNs and FSIs and extracted probabilities for the presence of these neurites as a function of distance from the soma. From these, we found the probabilities of intersection between the neurites of two neurons given their inter-somatic distance, and used these to construct three-dimensional striatal networks. These networks were examined for their predictions for the distributions of the numbers and distances of connections for all the connections in the microcircuit. We then combined the neuron models from a previous model (Humphries et al, 2009; ModelDB ID: 128874) with the new anatomical model. We used this new complete striatal model to examine the impact of the anatomical network on the firing properties of the MSN and FSI populations, and to study the influence of all the inputs to one MSN within the network. | ||

302. | Structure-dynamics relationships in bursting neuronal networks revealed (Mäki-Marttunen et al. 2013) | |

This entry includes tools for generating and analyzing network structure, and for running the neuronal network simulations on them. | ||

303. | Subiculum network model with dynamic chloride/potassium homeostasis (Buchin et al 2016) | |

This is the code implementing the single neuron and spiking neural network dynamics. The network has the dynamic ion concentrations of extracellular potassium and intracellular chloride. The code contains multiple parameter variations to study various mechanisms of the neural excitability in the context of chloride homeostasis. | ||

304. | Supervised learning in spiking neural networks with FORCE training (Nicola & Clopath 2017) | |

The code contained in the zip file runs FORCE training for various examples from the paper: Figure 2 (Oscillators and Chaotic Attractor) Figure 3 (Ode to Joy) Figure 4 (Song Bird Example) Figure 5 (Movie Example) Supplementary Figures 10-12 (Classifier) Supplementary Ode to Joy Example Supplementary Figure 2 (Oscillator Panel) Supplementary Figure 17 (Long Ode to Joy) Note that due to file size limitations, the supervisors for Figures 4/5 are not included. See Nicola, W., & Clopath, C. (2016). Supervised Learning in Spiking Neural Networks with FORCE Training. arXiv preprint arXiv:1609.02545. for further details. | ||

305. | Supervised learning with predictive coding (Whittington & Bogacz 2017) | |

"To effciently learn from feedback, cortical networks need to update synaptic weights on multiple levels of cortical hierarchy. An effective and well-known algorithm for computing such changes in synaptic weights is the error back-propagation algorithm. However, in the back-propagation algorithm, the change in synaptic weights is a complex function of weights and activities of neurons not directly connected with the synapse being modified, whereas the changes in biological synapses are determined only by the activity of pre-synaptic and post-synaptic neurons. Several models have been proposed that approximate the back-propagation algorithm with local synaptic plasticity, but these models require complex external control over the network or relatively complex plasticity rules. Here we show that a network developed in the predictive coding framework can efficiently perform supervised learning fully autonomously, employing only simple local Hebbian plasticity. ..." | ||

306. | Surround Suppression in V1 via Withdraw of Balanced Local Excitation in V1 (Shushruth 2012) | |

The model is mean-field network models, which is set up as a so-called ring-model, i. e. it is a highly idealized model of an orientation hypercolumn in primary visual cortex. Long-range intra-areal and inter-areal feedback connections are modeled phenomenologically as an external input. In this model, there are recurrent interactions via short-range local connections between orientation columns, but not between hypercolumns. | ||

307. | Sympathetic neuron (Wheeler et al 2004) | |

This study shows how synaptic convergence and plasticity can interact to generate synaptic gain in autonomic ganglia and thereby enhance homeostatic control. Using a conductance-based computational model of an idealized sympathetic neuron, we simulated the postganglionic response to noisy patterns of presynaptic activity and found that a threefold amplification in postsynaptic spike output can arise in ganglia, depending on the number and strength of nicotinic synapses, the presynaptic firing rate, the extent of presynaptic facilitation, and the expression of muscarinic and peptidergic excitation. See references for details. | ||

308. | Sympathetic Preganglionic Neurone (Briant et al. 2014) | |

A model of a sympathetic preganglionic neurone of muscle vasoconstrictor-type. | ||

309. | Synaptic Impairment, Robustness of Excitatory NNs w/ Different Topologies (Mirzakhalili et al 2017) | |

"Synaptic deficiencies are a known hallmark of neurodegenerative diseases, but the diagnosis of impaired synapses on the cellular level is not an easy task. Nonetheless, changes in the system-level dynamics of neuronal networks with damaged synapses can be detected using techniques that do not require high spatial resolution. This paper investigates how the structure/topology of neuronal networks influences their dynamics when they suffer from synaptic loss. We study different neuronal network structures/topologies by specifying their degree distributions. The modes of the degree distribution can be used to construct networks that consist of rich clubs and resemble small world networks, as well. We define two dynamical metrics to compare the activity of networks with different structures: persistent activity (namely, the self-sustained activity of the network upon removal of the initial stimulus) and quality of activity (namely, percentage of neurons that participate in the persistent activity of the network). Our results show that synaptic loss affects the persistent activity of networks with bimodal degree distributions less than it affects random networks. ..." | ||

310. | Synaptic plasticity can produce and enhance direction selectivity (Carver et al, 2008) | |

" ... We propose a parsimonious model of motion processing that generates direction selective responses using short-term synaptic depression and can reproduce salient features of direction selectivity found in a population of neurons in the midbrain of the weakly electric fish Eigenmannia virescens. The model achieves direction selectivity with an elementary Reichardt motion detector: information from spatially separated receptive fields converges onto a neuron via dynamically different pathways. In the model, these differences arise from convergence of information through distinct synapses that either exhibit or do not exhibit short-term synaptic depression—short-term depression produces phase-advances relative to nondepressing synapses. ..." | ||

311. | Synchronized oscillations of clock gene expression in the choroid plexus (Myung et al 2018) | |

Our model simulates synchronized rhythms in the clock gene expression found in the choroid plexus. These synchronized oscillations, primarily mediated by gap junctions, showed interesting relationships between their amplitude, oscillation frequency, and coupling strength (gap junction density) in our experimental data. The model is based on coupled Poincaré oscillators and replicates this phenomenon via a non-zero "twist" in each cell. | ||

312. | Synthesis of spatial tuning functions from theta cell spike trains (Welday et al., 2011) | |

A single compartment model reproduces the firing rate maps of place, grid, and boundary cells by receiving inhibitory inputs from theta cells. The theta cell spike trains are modulated by the rat's movement velocity in such a way that phase interference among their burst pattern creates spatial envelope function which simulate the firing rate maps. | ||

313. | Systems-level modeling of neuronal circuits for leech swimming (Zheng et al. 2007) | |

"This paper describes a mathematical model of the neuronal central pattern generator (CPG) that controls the rhythmic body motion of the swimming leech. The systems approach is employed to capture the neuronal dynamics essential for generating coordinated oscillations of cell membrane potentials by a simple CPG architecture with a minimal number of parameters. ... parameter estimation leads to predictions regarding the synaptic coupling strength and intrinsic period gradient along the nerve cord, the latter of which agrees qualitatively with experimental observations." | ||

314. | Temporal and spatial characteristics of vibrissa responses to motor commands (Simony et al. 2010) | |

"A mechanistic description of the generation of whisker movements is essential for understanding the control of whisking and vibrissal active touch. We explore how facial-motoneuron spikes are translated, via an intrinsic muscle, to whisker movements. This is achieved by constructing, simulating, and analyzing a computational, biomechanical model of the motor plant, and by measuring spiking to movement transformations at small and large angles using high-precision whisker tracking in vivo. ... The model provides a direct translation from motoneuron spikes to whisker movements and can serve as a building block in closed-loop motor–sensory models of active touch." | ||

315. | Thalamic network model of deep brain stimulation in essential tremor (Birdno et al. 2012) | |

"... Thus the decreased effectiveness of temporally irregular DBS trains is due to long pauses in the stimulus trains, not the degree of temporal irregularity alone. We also conducted computer simulations of neuronal responses to the experimental stimulus trains using a biophysical model of the thalamic network. Trains that suppressed tremor in volunteers also suppressed fluctuations in thalamic transmembrane potential at the frequency associated with cerebellar burst-driver inputs. Clinical and computational findings indicate that DBS suppresses tremor by masking burst-driver inputs to the thalamus and that pauses in stimulation prevent such masking. Although stimulation of other anatomic targets may provide tremor suppression, we propose that the most relevant neuronal targets for effective tremor suppression are the afferent cerebellar fibers that terminate in the thalamus." | ||

316. | Thalamic transformation of pallidal input (Hadipour-Niktarash 2006) | |

"In Parkinson’s disease, neurons of the internal segment of the globus pallidus (GPi) display the low-frequency tremor-related oscillations. These oscillatory activities are transmitted to the thalamic relay nuclei. Computer models of the interacting thalamocortical (TC) and thalamic reticular (RE) neurons were used to explore how the TC-RE network processes the low-frequency oscillations of the GPi neurons. ..." | ||

317. | The activity phase of postsynaptic neurons (Bose et al 2004) | |

We show, in a simplified network consisting of an oscillator inhibiting a follower neuron, how the interaction between synaptic depression and a transient potassium current in the follower neuron determines the activity phase of this neuron. We derive a mathematical expression to determine at what phase of the oscillation the follower neuron becomes active. This expression can be used to understand which parameters determine the phase of activity of the follower as the frequency of the oscillator is changed. See paper for more. | ||

318. | The ventricular AP and effects of the isoproterenol-induced cardiac hypertrophy (Sengul et al 2020) | |

This model reproduces Action Potential (AP) of Rat Ventricular Myocytes according to the experimental AP and Voltage Clamp recordings. | ||

319. | Theoretical principles of DBS induced synaptic suppression (Farokhniaee & McIntyre 2019) | |

"Deep brain stimulation (DBS) is a successful clinical therapy for a wide range of neurological disorders; however, the physiological mechanisms of DBS remain unresolved. While many different hypotheses currently exist, our analyses suggest that high frequency (~100?Hz) stimulation-induced synaptic suppression represents the most basic concept that can be directly reconciled with experimental recordings of spiking activity in neurons that are being driven by DBS inputs. Objective The goal of this project was to develop a simple model system to characterize the excitatory post-synaptic currents (EPSCs) and action potential signaling generated in a neuron that is strongly connected to pre-synaptic glutamatergic inputs that are being directly activated by DBS. Methods We used the Tsodyks-Markram (TM) phenomenological synapse model to represent depressing, facilitating, and pseudo-linear synapses driven by DBS over a wide range of stimulation frequencies. The EPSCs were then used as inputs to a leaky integrate-and-fire neuron model and we measured the DBS-triggered post-synaptic spiking activity. Results Synaptic suppression was a robust feature of high frequency stimulation, independent of the synapse type. As such, the TM equations were used to define alternative DBS pulsing strategies that maximized synaptic suppression with the minimum number of stimuli. ..." | ||

320. | Theta phase precession in a model CA3 place cell (Baker and Olds 2007) | |

"... The present study concerns a neurobiologically based computational model of the emergence of theta phase precession in which the responses of a single model CA3 pyramidal cell are examined in the context of stimulation by realistic afferent spike trains including those of place cells in entorhinal cortex, dentate gyrus, and other CA3 pyramidal cells. Spike-timing dependent plasticity in the model CA3 pyramidal cell leads to a spatially correlated associational synaptic drive that subsequently creates a spatially asymmetric expansion of the model cell’s place field. ... Through selective manipulations of the model it is possible to decompose theta phase precession in CA3 into the separate contributing factors of inheritance from upstream afferents in the dentate gyrus and entorhinal cortex, the interaction of synaptically controlled increasing afferent drive with phasic inhibition, and the theta phase difference between dentate gyrus granule cell and CA3 pyramidal cell activity." | ||

321. | Towards a biologically plausible model of LGN-V1 pathways (Lian et al 2019) | |

"Increasing evidence supports the hypothesis that the visual system employs a sparse code to represent visual stimuli, where information is encoded in an efficient way by a small population of cells that respond to sensory input at a given time. This includes simple cells in primary visual cortex (V1), which are defined by their linear spatial integration of visual stimuli. Various models of sparse coding have been proposed to explain physiological phenomena observed in simple cells. However, these models have usually made the simplifying assumption that inputs to simple cells already incorporate linear spatial summation. This overlooks the fact that these inputs are known to have strong non-linearities such as the separation of ON and OFF pathways, or separation of excitatory and inhibitory neurons. Consequently these models ignore a range of important experimental phenomena that are related to the emergence of linear spatial summation from non-linear inputs, such as segregation of ON and OFF sub-regions of simple cell receptive fields, the push-pull effect of excitation and inhibition, and phase-reversed cortico-thalamic feedback. Here, we demonstrate that a two-layer model of the visual pathway from the lateral geniculate nucleus to V1 that incorporates these biological constraints on the neural circuits and is based on sparse coding can account for the emergence of these experimental phenomena, diverse shapes of receptive fields and contrast invariance of orientation tuning of simple cells when the model is trained on natural images. The model suggests that sparse coding can be implemented by the V1 simple cells using neural circuits with a simple biologically plausible architecture." | ||

322. | TTX-R Na+ current effect on cell response (Herzog et al 2001) (MATLAB) | |

"Small dorsal root ganglion (DRG) neurons, which include nociceptors, express multiple voltage-gated sodium currents. In addition to a classical fast inactivating tetrodotoxin-sensitive (TTX-S) sodium current, many of these cells express a TTX-resistant (TTX-R) sodium current that activates near -70 mV and is persistent at negative potentials. To investigate the possible contributions of this TTX-R persistent (TTX-RP) current to neuronal excitability, we carried out computer simulations using the Neuron program with TTX-S and -RP currents, fit by the Hodgkin-Huxley model, that closely matched the currents recorded from small DRG neurons. ..." See paper for more and details. | ||

323. | Understanding odor information segregation in the olfactory bulb by MC/TCs (Polese et al. 2014) | |

Odor identification is one of the main tasks of the olfactory system. It is performed almost independently from the concentration of the odor providing a robust recognition. This capacity to ignore concentration information does not preclude the olfactory system from estimating concentration itself. Significant experimental evidence has indicated that the olfactory system is able to infer simultaneously odor identity and intensity. However, it is still unclear at what level or levels of the olfactory pathway this segregation of information occurs. In this work, we study whether this odor information segregation is performed at the input stage of the olfactory bulb: the glomerular layer. | ||

324. | V1 and AL spiking neural network for visual contrast response in mouse (Meijer et al. 2020) | |

This code contains the computational model included in Meijer et al., Cell Reports 2020, which reproduces some of the main experimental findings reported --most notably, the higher sensory response of secondary visual areas compared to that of primary visual areas for moderate visual contrast levels in mice. The model is based on a two-area spiking neural network with embedded short-term synaptic plasticity mechanisms. | ||

325. | Vesicular pool simulations of synaptic depression (Aristizabal and Glavinovic 2004) | |

"Synaptic release was simulated using a Simulink sequential storage model with three vesicular pools. Modeling was modular and easily extendable to the systems with greater number of vesicular pools, parallel input, or time-varying parameters. ... Finally, the method was tested experimentally using the rat phrenic-diaphragm neuromuscular junction." See paper for more and details. | ||

326. | Vestibulo-Ocular Reflex model in Matlab (Clopath at al. 2014) | |

" ... We then introduce a minimal model that consists of learning at the parallel fibers to Purkinje cells with the help of the climbing fibers. Although the minimal model reproduces the behavior of the wild-type animals and is analytically tractable, it fails at reproducing the behavior of mutant mice and the electrophysiology data. Therefore, we build a detailed model involving plasticity at the parallel fibers to Purkinje cells' synapse guided by climbing fibers, feedforward inhibition of Purkinje cells, and plasticity at the mossy fiber to vestibular nuclei neuron synapse. The detailed model reproduces both the behavioral and electrophysiological data of both the wild-type and mutant mice and allows for experimentally testable predictions. " | ||

327. | Voltage-based STDP synapse (Clopath et al. 2010) | |

Implementation of the STDP rule by Clopath et al., Nat. Neurosci. 13(3):344-352,2010 STDP mechanism added to the AlphaSynapse in NEURON. | ||

328. | Within movement adjustments of internal representations during reaching (Crevecoeur et al 2020) | |

"An important function of the nervous system is to adapt motor commands in anticipation of predictable disturbances, which supports motor learning when we move in novel environments such as force fields (FFs). Here, we show that movement control when exposed to unpredictable disturbances exhibit similar traits: motor corrections become tuned to the FF, and they evoke after effects within an ongoing sequence of movements. We propose and discuss the framework of adaptive control to explain these results: a real-time learning algorithm, which complements feedback control in the presence of model errors. This candidate model potentially links movement control and trial-by-trial adaptation of motor commands." | ||

329. | Working memory circuit with branched dendrites (Morita 2008) | |

This is a rate-coding model of the neocortical spatial working memory circuit incorporating multiple dendritic branches of the individual pyramidal cell in order to examine how nonlinear dendritic integration, combined with the nonuniform distribution of the external input, affects the behavior of the whole circuit. |