| | Models | Description |
| 1. |
A Moth MGC Model-A HH network with quantitative rate reduction (Buckley & Nowotny 2011)
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We provide the model used in Buckley & Nowotny (2011). It consists of a network of Hodgkin Huxley neurons coupled by slow GABA_B synapses which is run alongside a quantitative reduction described in the associated paper. |
| 2. |
Biophysically realistic neural modeling of the MEG mu rhythm (Jones et al. 2009)
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"Variations in cortical oscillations in the alpha (7–14 Hz) and beta (15–29 Hz) range have been correlated with attention, working memory, and stimulus detection. The mu rhythm recorded with magnetoencephalography (MEG) is a prominent oscillation generated by Rolandic cortex containing alpha and beta bands. Despite its prominence, the neural mechanisms regulating mu are unknown. We characterized the ongoing MEG mu rhythm from a localized source in the finger representation of primary somatosensory (SI) cortex. Subjects showed variation in the relative expression of mu-alpha or mu-beta, which were nonoverlapping for roughly 50% of their respective durations on single trials. To delineate the origins of this rhythm, a biophysically principled computational neural model of SI was developed, with distinct laminae, inhibitory and excitatory neurons, and feedforward (FF, representative of lemniscal thalamic drive) and feedback (FB, representative of higher-order cortical drive or input from nonlemniscal thalamic nuclei) inputs defined by the laminar location of their postsynaptic effects. ..." |
| 3. |
Ca+/HCN channel-dependent persistent activity in multiscale model of neocortex (Neymotin et al 2016)
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"Neuronal persistent activity has been primarily assessed in terms of electrical mechanisms, without attention to the complex array of molecular events that also control cell excitability. We developed a multiscale neocortical model proceeding from the molecular to the network level to assess the contributions of calcium regulation of hyperpolarization-activated cyclic nucleotide-gated (HCN) channels in providing additional and complementary support of continuing activation in the network. ..." |
| 4. |
CA1 network model for place cell dynamics (Turi et al 2019)
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Biophysical model of CA1 hippocampal region. The model simulates place cells/fields and explores the place cell dynamics as function of VIP+ interneurons. |
| 5. |
CA1 network model: interneuron contributions to epileptic deficits (Shuman et al 2020)
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Temporal lobe epilepsy causes significant cognitive deficits in both humans and rodents, yet the specific circuit mechanisms underlying these deficits remain unknown. There are profound and selective interneuron death and axonal reorganization within the hippocampus of both humans and animal models of temporal lobe epilepsy.
To assess the specific contribution of these mechanisms on spatial coding, we developed a biophysically constrained network model of the CA1 region that consists of different subtypes of interneurons. More specifically, our network consists of 150 cells, 130 excitatory pyramidal cells and 20 interneurons (Fig. 1A). To simulate place cell formation in the network model, we generated grid cell and place cell inputs from the Entorhinal Cortex (ECLIII) and CA3 regions, respectively, activated in a realistic manner as observed when an animal transverses a linear track. Realistic place fields emerged in a subpopulation of pyramidal cells (40-50%), in which similar EC and CA3 grid cell inputs converged onto distal/proximal apical and basal dendrites. The tuning properties of these cells are very similar to the ones observed experimentally in awake, behaving animals
To examine the role of interneuron death and axonal reorganization in the formation and/or tuning properties of place fields we selectively varied the contribution of each interneuron type and desynchronized the two excitatory inputs. We found that desynchronized inputs were critical in reproducing the experimental data, namely the profound reduction in place cell numbers, stability and information content. These results demonstrate that the desynchronized firing of hippocampal neuronal populations contributes to poor spatial processing in epileptic mice, during behavior. Given the lack of experimental data on the selective contributions of interneuron death and axonal reorganization in spatial memory, our model findings predict the mechanistic effects of these alterations at the cellular and network levels. |
| 6. |
Coding of stimulus frequency by latency in thalamic networks (Golomb et al 2005)
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The paper presents models of the rat vibrissa processing system including the
posterior medial (POm) thalamus, ventroposterior medial (VPm) thalamus, and GABAB-
mediated feedback inhibition from the reticular thalamic (Rt) nucleus.
A clear match between the experimentally measured spike-rates and the
numerically calculated rates for the full model occurs when VPm thalamus receives stronger
brainstem input and weaker GABAB-mediated inhibition than POm thalamus. |
| 7. |
Composite spiking network/neural field model of Parkinsons (Kerr et al 2013)
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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. |
| 8. |
Current Dipole in Laminar Neocortex (Lee et al. 2013)
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Laminar neocortical model in NEURON/Python, adapted from Jones et al 2009.
https://bitbucket.org/jonescompneurolab/corticaldipole |
| 9. |
Dynamic cortical interlaminar interactions (Carracedo et al. 2013)
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"... Here we demonstrate the mechanism underlying a purely neocortical delta rhythm generator and show a remarkable laminar, cell subtype and local subcircuit delineation between delta
and nested theta rhythms. We show that spike timing during delta-nested theta rhythms controls an iterative, reciprocal interaction between deep and superficial cortical layers resembling the unsupervised learning processes proposed for laminar neural networks by Hinton and colleagues ... and mimicking the alternating cortical dynamics of sensory and memory processing during wakefulness."
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| 10. |
Effects of increasing CREB on storage and recall processes in a CA1 network (Bianchi et al. 2014)
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Several recent results suggest that boosting the CREB pathway improves hippocampal-dependent memory in healthy rodents and restores this type of
memory in an AD mouse model. However, not much is known about how CREB-dependent neuronal alterations in synaptic strength, excitability and
LTP can boost memory formation in the complex architecture of a neuronal network. Using a model of a CA1 microcircuit, we investigate whether
hippocampal CA1 pyramidal neuron properties altered by increasing CREB activity may contribute to improve memory storage and recall. With a set of patterns presented to a network, we find that the pattern recall quality under AD-like conditions is significantly better when boosting CREB function with respect to control. The results are robust and consistent upon increasing the synaptic damage expected by AD progression, supporting the idea that the use of CREB-based therapies could provide a new approach
to treat AD. |
| 11. |
Efficient simulation environment for modeling large-scale cortical processing (Richert et al. 2011)
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"We have developed a spiking neural network simulator, which is both easy to use and computationally efficient, for the generation of large-scale computational neuroscience models. The simulator implements current or conductance based Izhikevich neuron networks, having spike-timing dependent plasticity and short-term plasticity. ..." |
| 12. |
Hippocampal CA1 NN with spontaneous theta, gamma: full scale & network clamp (Bezaire et al 2016)
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This model is a full-scale, biologically constrained rodent hippocampal CA1 network model that includes 9 cells types (pyramidal cells and 8 interneurons) with realistic proportions of each and realistic connectivity between the cells. In addition, the model receives realistic numbers of afferents from artificial cells representing hippocampal CA3 and entorhinal cortical layer III. The model is fully scaleable and parallelized so that it can be run at small scale on a personal computer or large scale on a supercomputer. The model network exhibits spontaneous theta and gamma rhythms without any rhythmic input. The model network can be perturbed in a variety of ways to better study the mechanisms of CA1 network dynamics. Also see online code at http://bitbucket.org/mbezaire/ca1 and further information at http://mariannebezaire.com/models/ca1 |
| 13. |
Knox implementation of Destexhe 1998 spike and wave oscillation model (Knox et al 2018)
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" ...The aim of this study was to use an established thalamocortical computer model to determine how T-type calcium channels work in concert with cortical excitability to contribute to pathogenesis and treatment response in CAE.
METHODS:
The model is comprised of cortical pyramidal, cortical inhibitory, thalamocortical relay, and thalamic reticular single-compartment neurons, implemented with Hodgkin-Huxley model ion channels and connected by AMPA, GABAA , and GABAB synapses. Network behavior was simulated for different combinations of T-type calcium channel conductance, inactivation time, steady state activation/inactivation shift, and cortical GABAA conductance.
RESULTS:
Decreasing cortical GABAA conductance and increasing T-type calcium channel conductance converted spindle to spike and wave oscillations; smaller changes were required if both were changed in concert. In contrast, left shift of steady state voltage activation/inactivation did not lead to spike and wave oscillations, whereas right shift reduced network propensity for oscillations of any type...." |
| 14. |
L5 PFC microcircuit used to study persistent activity (Papoutsi et al. 2014, 2013)
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Using a heavily constrained biophysical model of a L5 PFC microcircuit we investigate the mechanisms that underlie persistent activity emergence (ON) and termination (OFF) and search for the minimum network size required for expressing these states within physiological regimes. |
| 15. |
MEG of Somatosensory Neocortex (Jones et al. 2007)
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"... To make a direct and principled connection between the SI (somatosensory primary neocortex magnetoencephalography) waveform and underlying neural dynamics, we developed a biophysically realistic
computational SI model that contained excitatory and inhibitory neurons in supragranular and infragranular layers. ... our model
provides a biophysically realistic solution to the MEG signal and can predict the electrophysiological correlates of human perception."
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| 16. |
Motor system model with reinforcement learning drives virtual arm (Dura-Bernal et al 2017)
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"We implemented a model of the motor system with the following components: dorsal premotor cortex (PMd), primary motor cortex (M1), spinal cord and musculoskeletal arm (Figure 1). PMd modulated M1 to select the target to reach, M1 excited the descending spinal cord neurons that drove the arm muscles, and received arm proprioceptive feedback (information about the arm position) via the ascending spinal cord neurons.
The large-scale model of M1 consisted of 6,208 spiking Izhikevich model neurons [37] of four types: regular-firing and bursting pyramidal neurons, and fast-spiking and low-threshold-spiking interneurons. These were distributed across cortical layers 2/3, 5A, 5B and 6, with cell properties, proportions, locations, connectivity, weights and delays drawn primarily from mammalian experimental data [38], [39], and described in detail in previous work [29]. The network included 486,491 connections, with synapses modeling properties of four different receptors ..." |
| 17. |
Multiscale model of primary motor cortex circuits predicts in vivo dynamics (Dura-Bernal et al 2023)
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Understanding cortical function requires studying multiple scales: molecular, cellular, circuit and behavior. We developed a multiscale biophysically-detailed model of mouse primary motor cortex (M1) with over 10,000 neurons and 30 million synapses. Neuron types, densities, spatial distributions, morphologies, biophysics, connectivity and dendritic synapse locations were constrained by experimental data. The model includes long-range inputs from seven thalamic and cortical regions, and noradrenergic inputs. Connectivity depends on cell class and cortical depth at sublaminar resolution. The model accurately predicted in vivo layer- and cell type-specific responses (firing rates and LFP) associated with behavioral states (quiet wakefulness and movement) and experimental manipulations (noradrenaline receptor blockade and thalamus inactivation). We generated mechanistic hypotheses underlying the observed activity and analyzed low-dimensional population latent dynamics. This quantitative theoretical framework can be used to integrate and interpret M1 experimental data and sheds light on the cell type-specific multiscale dynamics associated with several experimental conditions and behaviors. |
| 18. |
Multitarget pharmacology for Dystonia in M1 (Neymotin et al 2016)
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" ... We developed a multiscale model of primary motor cortex, ranging from molecular, up to cellular, and network levels, containing 1715 compartmental model neurons with multiple ion channels and intracellular molecular dynamics. We wired the model based on electrophysiological data obtained from mouse motor cortex circuit mapping experiments. We used the model to reproduce patterns of heightened activity seen in dystonia by applying independent random variations in parameters to identify pathological parameter sets. ..." |
| 19. |
NMDAR & GABAB/KIR Give Bistable Dendrites: Working Memory & Sequence Readout (Sanders et al., 2013)
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" ...Here, we show that the voltage dependence of the inwardly rectifying potassium (KIR) conductance activated by GABA(B) receptors adds substantial robustness to network simulations of bistability and the persistent firing that it underlies. The hyperpolarized state is robust because, at hyperpolarized potentials, the GABA(B)/KIR conductance is high and the NMDA conductance is low; the depolarized state is robust because, at depolarized potentials, the NMDA conductance is high and the GABA(B)/KIR conductance is low. Our results suggest that this complementary voltage dependence of GABA(B)/KIR and NMDA conductances makes them a "perfect couple" for producing voltage bistability." |
| 20. |
Pyramidal neuron, fast, regular, and irregular spiking interneurons (Konstantoudaki et al 2014)
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This is a model network of prefrontal cortical microcircuit based primarily on rodent data. It includes 16 pyramidal model neurons, 2 fast spiking interneuron models, 1 regular spiking interneuron model and 1 irregular spiking interneuron model. The goal of the paper was to use this model network to determine the role of specific interneuron subtypes in persistent activity |
| 21. |
Simulations of oscillations in piriform cortex (Wilson & Bower 1992)
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"1. A large-scale computer model of the piriform cortex was
constructed on the basis of the known anatomic and physiological
organization of this region.
2. The oscillatory field potential and electroencephalographic
(EEG) activity generated by the model was compared with actual
physiological results. The model was able to produce patterns of
activity similar to those recorded physiologically in response to
both weak and strong electrical shocks to the afferent input. The
model also generated activity patterns similar to EEGs recorded in
behaving animals.
3. ..." |
| 22. |
Sleep-wake transitions in corticothalamic system (Bazhenov et al 2002)
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The authors investigate the transition between sleep and awake states with intracellular recordings in cats and computational models. The model describes many essential features of slow wave sleep and activated states as well as the transition between them. |
| 23. |
Stoney vs Histed: Quantifying spatial effects of intracortical microstims (Kumaravelu et al 2022)
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"...We implemented a biophysically-based computational model of a cortical column comprising neurons with realistic morphology and representative synapses. We quantified the spatial effects of single pulses and short trains of ICMS, including the volume of activated neurons and the density of activated neurons as a function of stimulation intensity..." |
| 24. |
Synchrony by synapse location (McTavish et al. 2012)
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This model considers synchrony between mitral cells induced via shared
granule cell interneurons while taking into account the spatial
constraints of the system. In particular, since inhibitory inputs
decay passively along the lateral dendrites, this model demonstrates
that an optimal arrangement of the inhibitory synapses will be near
the cell bodies of the relevant mitral cells. |
| 25. |
Thalamic network model of deep brain stimulation in essential tremor (Birdno et al. 2012)
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"... 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."
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| 26. |
Thalamic Reticular Network (Destexhe et al 1994)
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Demo for simulating networks of thalamic reticular neurons (reproduces figures from Destexhe A et al 1994) |
| 27. |
Thalamic transformation of pallidal input (Hadipour-Niktarash 2006)
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"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. ..."
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| 28. |
Thalamocortical and Thalamic Reticular Network (Destexhe et al 1996)
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NEURON model of oscillations in networks of thalamocortical and thalamic reticular neurons in the ferret. (more applications for a model quantitatively identical to previous DLGN model; updated for NEURON v4 and above) |
| 29. |
Thalamocortical augmenting response (Bazhenov et al 1998)
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In the cortical model, augmenting responses were more powerful in the "input" layer compared with those in the "output" layer. Cortical stimulation of the network model produced augmenting responses in cortical neurons in distant cortical areas through corticothalamocortical loops and low-threshold intrathalamic augmentation. ... The predictions of the model were compared with in vivo recordings from neurons in cortical area 4 and thalamic ventrolateral nucleus of anesthetized cats. The known intrinsic properties of thalamic cells and thalamocortical interconnections can account for the basic properties of cortical augmenting responses. See reference for details. NEURON implementation note: cortical SU cells are getting slightly too little stimulation - reason unknown. |
| 30. |
Thalamocortical control of propofol phase-amplitude coupling (Soplata et al 2017)
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"The anesthetic propofol elicits many different spectral properties on the EEG, including alpha oscillations (8-12 Hz), Slow Wave Oscillations (SWO, 0.1-1.5 Hz), and dose-dependent phase-amplitude coupling (PAC) between alpha and SWO. Propofol is known to increase GABAA inhibition and decrease H-current strength, but how it generates these rhythms and their interactions is still unknown. To investigate both generation of the alpha rhythm and its PAC to SWO, we simulate a Hodgkin-Huxley network model of a hyperpolarized thalamus and corticothalamic inputs. ..." |
| 31. |
Turtle visual cortex model (Nenadic et al. 2003, Wang et al. 2005, Wang et al. 2006)
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This is a model of the visual cortex of freshwater turtles that is based upon the
known anatomy and physiology of individual neurons. The model was published in three
papers (Nenadic et al., 2003; Wang et al., 2005; Wang et al., 2006), which should be
consulted for full details on its construction. The model has also been used in several
papers (Robbins and Senseman, 2004; Du et al., 2005; Du et al., 2006). It is
implemented in GENESIS (Bower and Beeman, 1998). |
