| | Models | Description |
| 1. |
A focal seizure model with ion concentration changes (Gentiletti et al., 2022)
|
|
|
Computer model was used to investigate the possible mechanisms of seizure initiation, progression and termination. The model was developed by complementing the Hodgkin-Huxley equations with activity-dependent changes in intra- and extracellular ion concentrations. The model incorporates a number of ionic mechanisms such as: active and passive membrane currents, inhibitory synaptic GABAA currents, Na/K pump, KCC2 cotransporter, glial K buffering, radial diffusion between extracellular space and bath, and longitudinal diffusion between dendritic and somatic compartments in pyramidal cells. |
| 2. |
A Moth MGC Model-A HH network with quantitative rate reduction (Buckley & Nowotny 2011)
|
|
|
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. |
| 3. |
CA1 network model for place cell dynamics (Turi et al 2019)
|
|
|
Biophysical model of CA1 hippocampal region. The model simulates place cells/fields and explores the place cell dynamics as function of VIP+ interneurons. |
| 4. |
Changes of ionic concentrations during seizure transitions (Gentiletti et al. 2016)
|
|
|
"... In order to
investigate the respective roles of synaptic interactions and
nonsynaptic mechanisms in seizure transitions, we developed a
computational model of hippocampal cells, involving the extracellular
space, realistic dynamics of Na+, K+, Ca2+ and Cl - ions, glial uptake
and extracellular diffusion mechanisms. We show that the network
behavior with fixed ionic concentrations may be quite different from
the neurons’ behavior when more detailed modeling of ionic dynamics is
included. In particular, we show that in the extended model strong
discharge of inhibitory interneurons may result in long lasting
accumulation of extracellular K+, which sustains the depolarization of
the principal cells and causes their pathological discharges.
..."
|
| 5. |
Leech Heart (HE) Motor Neuron conductances contributions to NN activity (Lamb & Calabrese 2013)
|
|
|
"...
To explore the relationship between conductances,
and in particular how they influence the activity of motor neurons in
the well characterized leech heartbeat system, we developed a new
multi-compartmental Hodgkin-Huxley style leech heart motor neuron
model.
To do so, we evolved a population of model instances, which
differed in the density of specific conductances, capable of achieving
specific output activity targets given an associated input pattern.
...
We found that the strengths of many conductances,
including those with differing dynamics, had strong partial
correlations and that these relationships appeared to be linked by
their influence on heart motor neuron activity.
Conductances that had
positive correlations opposed one another and had the opposite effects
on activity metrics when perturbed whereas conductances that had
negative correlations could compensate for one another and had similar
effects on activity metrics.
" |
| 6. |
NMDAR & GABAB/KIR Give Bistable Dendrites: Working Memory & Sequence Readout (Sanders et al., 2013)
|
|
|
" ...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." |
| 7. |
Simulated cortical color opponent receptive fields self-organize via STDP (Eguchi et al., 2014)
|
|
|
"...
In this work, we address the problem of understanding the cortical processing of color information with a possible mechanism of the development of the patchy distribution of color selectivity via computational modeling.
...
Our model of the early visual system consists of multiple topographically-arranged layers of excitatory and inhibitory neurons, with sparse intra-layer connectivity and feed-forward connectivity between layers.
Layers are arranged based on anatomy of early visual pathways, and include a retina, lateral geniculate nucleus, and layered neocortex.
...
After training with natural images, the neurons display heightened sensitivity to specific colors.
..." |
| 8. |
Single compartment Dorsal Lateral Medium Spiny Neuron w/ NMDA and AMPA (Biddell and Johnson 2013)
|
|
|
A biophysical single compartment model of the dorsal lateral striatum medium spiny neuron is presented here. The model is an implementation then adaptation of a previously described model (Mahon et al. 2002). The model has been adapted to include NMDA and AMPA receptor models that have been fit to dorsal lateral striatal neurons. The receptor models allow for excitation by other neuron models. |
| 9. |
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.
..."
|
| 10. |
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. |
