| | Models | Description |
| 1. |
A cortical sheet mesoscopic model for investigating focal seizure onset dynamics (Wang et al. 2014)
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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. |
| 2. |
A neural mass model for critical assessment of brain connectivity (Ursino et al 2020)
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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. |
| 3. |
CA1 pyramidal neuron: as a 2-layer NN and subthreshold synaptic summation (Poirazi et al 2003)
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We developed a CA1
pyramidal cell model calibrated with a broad spectrum of in vitro data. Using simultaneous
dendritic and somatic recordings, and combining results for two different response measures
(peak vs. mean EPSP), two different stimulus formats (single shock vs. 50 Hz trains),
and two different spatial integration conditions (within vs. between-branch summation),
we found the cell's subthreshold responses to paired inputs are best described as a sum of
nonlinear subunit responses, where the subunits correspond to different dendritic branches.
In addition to suggesting a new type of experiment and providing testable predictions, our
model shows how conclusions regarding synaptic arithmetic can be influenced by an array
of seemingly innocuous experimental design choices. |
| 4. |
DRG neuron models investigate how ion channel levels regulate firing properties (Zheng et al 2019)
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We present computational models for an Abeta-LTMR (low-threshold mechanoreceptor) and a C-LTMR expressing four Na channels and four K channels to investigate how the expression level of Kv1 and Kv4 regulate number of spikes (repetitive firing) and onset latency to action potentials in Abeta-LTMRs and C-LTMRs, respectively.
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| 5. |
Duration-tuned neurons from the inferior colliculus of the big brown bat (Aubie et al. 2009)
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dtnet is a generalized neural network simulator written in C++ with an easy to use XML description language to generate arbitrary neural networks and then run simulations covering many different parameter values. For example, you can specify ranges of parameter values for several different connection weights and then automatically run simulations over all possible parameters. Graphing ability is built in as long as the free, open-source, graphing application GLE (http://glx.sourceforge.net/) is installed.
Included in the examples folder are simulation descriptions that were used to generate the results in Aubie et al. (2009). Refer to the README file for instructions on compiling and running these examples.
The most recent source code can be obtained from GitHub: <a href="https://github.com/baubie/dtnet">https://github.com/baubie/dtnet</a>
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| 6. |
Dynamical model of olfactory bulb mitral cell (Rubin, Cleland 2006)
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This four-compartment mitral cell exhibits endogenous subthreshold oscillations, phase resetting, and evoked spike phasing properties as described in electrophysiological studies of mitral cells. It is derived from the prior work of Davison et al (2000) and Bhalla and Bower (1993). See readme.txt for details. |
| 7. |
Effect of slowly inactivating IKdr to delayed firing of action potentials (Wu et al. 2008)
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"The properties of slowly inactivating delayed-rectifier K+ current (IKdr) were investigated in NG108-15 neuronal cells differentiated with long-term exposure to dibutyryl cyclic AMP.
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The computer model, in which state-dependent inactivation of IKdr was incorporated, was also implemented to predict the firing behavior present in NG108-15 cells.
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Our theoretical work and the experimental results led us to propose a pivotal role of slowly inactivating IKdr in delayed firing of APs in NG108-15 cells. The results also suggest that aconitine modulation of IKdr gating is an important molecular mechanism through which it can contribute to neuronal firing." |
| 8. |
Effects of Dopamine Modulation and KIR Inactivation in NAc Medium Spiny Neurons (Steephen 2011)
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Due to the involvement of nucleus accumbens (NAc) medium spiny neurons (MSNs) in diverse behaviors, their excitability changes can have broad functional significance. Dopamine modulates the biophysical behavior of MSNs. In ~40% of MSNs, inward rectifying potassium (KIR) currents inactivate significantly, imparting greater excitability. Employing a 189-compartment computational model of the MSN and using spatiotemporally distributed synaptic inputs, the regulation of excitability by KIR inactivation and dopaminergic modulation was investigated and quantitatively characterized. It was shown that by forming different combinations, these regulating agents could fine tune MSN excitability across a wide range. With existing evidence indicating MSNs with and without KIR inactivation to be the likely targets for D2- and D1-receptor mediated modulations, respectively, the present findings suggest that dopaminergic channel modulation may intensify the existing excitability difference between them by suppressing the excitability of MSNs without KIR inactivation while further enhancing the excitability of the more excitable MSNs with KIR inactivation. On the other hand, the combined modulation of channels and synapses by dopamine may reverse the relative excitability of one cell type with respect to the other.
This model contains a complete biophysical model of MSN cell. The application allows the user to vary the cell properties by choosing the type of KIR channels included (inKIR or non-inKIR), the type of Dopamine receptors (D1R or D2R) and the modulation mechanism (Intrinsic modulation , Intrinsic-synaptic modulation, or No modulation). The user can also choose between the single pulse current clamp stimulation or a physiologically realistic synaptic stimulation scheme. More details are available in the Help provided with the application. |
| 9. |
Effects of KIR current inactivation in NAc Medium Spiny Neurons (Steephen and Manchanda 2009)
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"Inward rectifying potassium (KIR) currents in medium spiny (MS) neurons of nucleus accumbens inactivate significantly in ~40% of the neurons but not in the rest, which may lead to differences in input processing by these two groups.
Using a 189-compartment computational model of the MS neuron, we investigate the influence of this property using injected current as well as spatiotemporally distributed synaptic inputs.
Our study demonstrates that KIR current inactivation facilitates depolarization, firing frequency and firing onset in these neurons. ..." |
| 10. |
Escape response latency in the Giant Fiber System of Drosophila melanogastor (Augustin et al 2019)
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"The Giant Fiber System (GFS) is a multi-component neuronal pathway mediating rapid escape response in the adult fruit-fly Drosophila melanogaster, usually in the face of a threatening visual stimulus. Two branches of the circuit promote the response by stimulating an escape jump followed by flight initiation. Our recent work demonstrated an age-associated decline in the speed of signal propagation through the circuit, measured as the stimulus-to-muscle depolarization response latency. The decline is likely due to the diminishing number of inter-neuronal gap junctions in the GFS of ageing flies. In this work, we presented a realistic conductance-based, computational model of the GFS that recapitulates our experimental results and identifies some of the critical anatomical and physiological components governing the circuit's response latency. According to our model, anatomical properties of the GFS neurons have a stronger impact on the transmission than neuronal membrane conductance densities. The model provides testable predictions for the effect of experimental interventions on the circuit's performance in young and ageing flies." |
| 11. |
Evolving simple models of diverse dynamics in hippocampal neuron types (Venkadesh et al 2018)
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" ... we present an automated pipeline based on evolutionary algorithms to quantitatively reproduce features of various classes of neuronal spike patterns using the Izhikevich model. Employing experimental data from Hippocampome.org, a comprehensive knowledgebase of neuron types in the rodent hippocampus, we demonstrate that our approach reliably fit Izhikevich models to nine distinct classes of experimentally recorded spike patterns, including
delayed spiking, spiking with adaptation, stuttering, and bursting. ..." |
| 12. |
Fast-spiking cortical interneuron (Golomb et al. 2007)
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Cortical fast-spiking (FS) interneurons display highly variable electrophysiological properties. We hypothesize that this variability emerges naturally if one assumes a continuous distribution of properties in a small set of active channels. We construct a minimal, single-compartment conductance-based model of FS cells that includes transient Na+, delayed-rectifier K+, and slowly inactivating d-type K+ conductances. The model may display delay to firing. Stuttering (elliptic bursting) and subthreshold oscillations may be observed for small Na+ window current. |
| 13. |
High frequency oscillations induced in three gap-junction coupled neurons (Tseng et al. 2008)
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Here we showed experimentally that high frequency oscillations (up to 600 Hz) were easily induced in a purely gap-junction coupled network by simple two stimuli with very short interval. The root cause is that the second elicited spike suffered from slow propagation speed and failure to transmit through a low-conductance junction. Similiar results were also obtained in these simulation. |
| 14. |
Hippocampal basket cell gap junction network dynamics (Saraga et al. 2006)
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2 cell network of hippocampal basket cells connected by gap junctions. Paper explores how distal gap junctions and active dendrites can tune network dynamics. |
| 15. |
Inhibition and glial-K+ interaction leads to diverse seizure transition modes (Ho & Truccolo 2016)
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"How focal seizures initiate and evolve in human neocortex remains a fundamental problem in neuroscience. Here, we use biophysical neuronal network models of neocortical patches to study how the interaction between inhibition and extracellular potassium ([K+]o) dynamics may contribute to different types of focal seizures. Three main types of propagated focal seizures observed in recent intracortical microelectrode recordings in humans were modelled ..." |
| 16. |
Neural Mass Model for relationship between Brain Rhythms + Functional Connectivity (Ricci et al '21)
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The Neural Mass Model (NMM) generates biologically reliable mean field potentials of four interconnected regions of interest (ROIs) of the cortex, each simulating a different brain rhythm (in theta, alpha, beta and gamma ranges). These neuroelectrical signals originate from the assumption that ROIs influence each other via of excitatory or by-synaptic inhibitory connections. Besides receiving long-range synapses from other ROIs, each one receives an external input and superimposed Gaussian white noise. We used the NMM to simulate different connectivity networks of four ROIs, by varying both the synaptic strengths and the inputs. The purpose of this study is to investigate how the transmission of brain rhythms behaves under linear and nonlinear conditions. To this aim, we investigated the performance of eight Functional Connectivity (FC) estimators (Correlation, Delayed Correlation, Coherence, Lagged Coherence, Temporal Granger Causality, Spectral Granger Causality, Phase Synchronization and Transfer Entropy) in detecting the connectivity network changes. Results suggest that when a ROI works in the linear region, its capacity to transmit its rhythm increases, while when it saturates, the oscillatory activity becomes strongly affected by other ROIs. Software included here allows the simulation of mean field potentials of four interconnected ROIs, their visualization, both in time and frequency domains, and the estimation of the related FC with eight different methods (for Transfer Entropy the Trentool package is needed). |
| 17. |
Norns - Neural Network Studio (Visser & Van Gils 2014)
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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. |
| 18. |
Olfactory bulb network model of gamma oscillations (Bathellier et al. 2006; Lagier et al. 2007)
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This model implements a network of 100 mitral cells connected with
asynchronous inhibitory "synapses" that is meant to reproduce the
GABAergic transmission of ensembles of connected granule cells.
For appropriate parameters of this special synapse the model generates
gamma oscillations with properties very similar to what is observed
in olfactory bulb slices (See Bathellier et al. 2006, Lagier et al. 2007).
Mitral cells are modeled as single compartment neurons with a small
number of different voltage gated channels. Parameters were tuned to reproduce the
fast subthreshold oscillation of the membrane potential observed experimentally
(see Desmaisons et al. 1999).
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| 19. |
Online learning model of olfactory bulb external plexiform layer network (Imam & Cleland 2020)
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This model illustrates the rapid online learning of odor representations, and their recognition despite high levels of interference (other competing odorants), in a model of the olfactory bulb external plexiform layer (EPL) network. The computational principles embedded in this model are based on the those developed in the biophysical models of Li and Cleland (2013, 2017).
This is a standard Python version of a model written for Intel's Loihi neuromorphic hardware platform (The Loihi code is available at https://github.com/intel-nrc-ecosystem/models/tree/master/official/epl). |
| 20. |
Robust modulation of integrate-and-fire models (Van Pottelbergh et al 2018)
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"By controlling the state of neuronal populations, neuromodulators ultimately affect behavior. A key neuromodulation mechanism is the alteration of neuronal excitability via the modulation of ion channel expression. This type of neuromodulation is normally studied with conductance-based models, but those models are computationally challenging for large-scale network simulations needed in population studies. This article studies the modulation properties of the multiquadratic integrate-and-fire model, a generalization of the classical quadratic integrate-and-fire model. The model is shown to combine the computational economy of integrate-and-fire modeling and the physiological interpretability of conductance-based modeling. It is therefore a good candidate for affordable computational studies of neuromodulation in large networks." |
| 21. |
Tonic neuron in spinal lamina I: prolongation of subthreshold depol. (Prescott and De Koninck 2005)
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Model demonstrates mechanism whereby two kinetically distinct inward currents act synergistically to prolong subthreshold depolarization. The important currents are a persistent Na current (with fast kinetics) and a persistent Ca current (with slower kinetics). Model also includes a slow K current and transient Ca current, in addition to standard HH currents. Model parameters are set to values used in Fig. 8A. Simulation shows prolonged depolarizations in response to two brief stimuli.
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