| | Models | Description |
| 1. |
A focal seizure model with ion concentration changes (Gentiletti et al., 2022)
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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)
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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. |
| 3. |
Alcohol action in a detailed Purkinje neuron model and an efficient simplified model (Forrest 2015)
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" ... we employ a
novel reduction algorithm to produce a 2 compartment model of the cerebellar Purkinje
neuron from a previously published, 1089 compartment model. It runs more than 400 times
faster and retains the electrical behavior of the full model. So, it is more suitable for inclusion
in large network models, where computational power is a limiting issue. We show the utility
of this reduced model by demonstrating that it can replicate the full model’s response to
alcohol, which can in turn reproduce experimental recordings from Purkinje neurons
following alcohol application.
..." |
| 4. |
An ion-based model for swelling of neurons and astrocytes (Hubel & Ullah 2016)
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The programs describe ion dynamics and osmosis-driven cellular swelling. “code_fig3.ode” shows a
scenario of permanent cessation of energy supply / Na/K-pump activity, and the induced transition from
normal conditions to the Donnan equilibrium for an isolated neuron and its extracellular space.
“code_Fig7.ode” shows spreading depolarization induced by an interruption of energy supply in a model
consisting of a neuron, a glia cell and the extracellular space. The simulations show the evolution of ion
concentrations, Nernst potentials, the membrane potential, gating variables and cellular volumes. |
| 5. |
Anoxic depolarization, recovery: effect of brain regions and extracellular space (Hubel et al. 2016)
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The extent of anoxic depolarization (AD), the initial electrophysiological event during ischemia, determines the degree of brain region-specific neuronal damage. Neurons in higher brain regions have stronger ADs and are more easily injured than neurons in lower brain region. The mechanism leading to such differences is not clear. We use a computational model based on a Hodgkin-Huxley framework which includes neural spiking dynamics, processes of ion accumulation, and homeostatic mechanisms like vascular coupling and Na/K-exchange pumps. We show that a large extracellular space (ECS) explains the recovery failure in high brain regions. A phase-space analysis shows that with a large ECS recovery from AD through potassium regulation is impossible. The code 'time_series.ode' can be used to simulate AD for a large and a small ECS and show the different behaviors. The code ‘continuations.ode’ can be used to show the fixed point structure. Depending on our choice of large or small ECS the fixed point curve implies the presence/absence of a recovery threshold that defines the potassium clearance demand. |
| 6. |
AOB mitral cell: persistent activity without feedback (Zylbertal et al., 2015)
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Persistent activity has been reported in many brain areas and is
hypothesized to mediate working memory and emotional brain states and
to rely upon network or biophysical feedback. Here we demonstrate a
novel mechanism by which persistent neuronal activity can be generated
without feedback, relying instead on the slow removal of Na+ from
neurons following bursts of activity. This is a realistic
conductance-based model that was constructed using the detailed
morphology of a single typical accessory olfactory bulb (AOB) mitral
cell for which the electrophysiological properties were
characterized. |
| 7. |
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. |
| 8. |
Calcium response prediction in the striatal spines depending on input timing (Nakano et al. 2013)
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We construct an electric compartment model of the striatal medium spiny neuron with a realistic morphology and predict the calcium responses in the synaptic spines with variable timings of the glutamatergic and dopaminergic inputs and the postsynaptic action potentials.
The model was validated by reproducing the responses to current inputs and could predict the electric and calcium responses to glutamatergic inputs and back-propagating action potential in the proximal and distal synaptic spines during up and down states. |
| 9. |
Cerebellar granule cell (Masoli et al 2020)
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"The cerebellar granule cells (GrCs) are classically described as a homogeneous neuronal population discharging
regularly without adaptation. We show that GrCs in fact generate diverse response patterns to current injection
and synaptic activation, ranging from adaptation to acceleration of firing. Adaptation was predicted by parameter
optimization in detailed computational models based on available knowledge on GrC ionic channels. The models also
predicted that acceleration required additional mechanisms. We found that yet unrecognized TRPM4 currents specifically
accounted for firing acceleration and that adapting GrCs outperformed accelerating GrCs in transmitting high-frequency
mossy fiber (MF) bursts over a background discharge. This implied that GrC subtypes identified by their
electroresponsiveness corresponded to specific neurotransmitter release probability values. Simulations showed
that fine-tuning of pre- and post-synaptic parameters generated effective MF-GrC transmission channels, which
could enrich the processing of input spike patterns and enhance spatio-temporal recoding at the cerebellar input stage." |
| 10. |
Changes of ionic concentrations during seizure transitions (Gentiletti et al. 2016)
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"... 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.
..."
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| 11. |
Conductance-based model of rodent thoracic sympathetic postganglionic neuron (McKinnon et al 2019)
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"Thoracic sympathetic postganglionic neurons (tSPNs) represent the final neural output for control of vasomotor and thermoregulatory function. We used whole-cell recordings and computational modeling to provide broad insight on intrinsic cellular mechanisms controlling excitability and capacity for synaptic integration. Compared to past intracellular recordings using microelectrode impalement, we observed dramatically higher membrane resistivity with primacy in controlling enhanced tSPN excitability and recruitment via synaptic integration. Compared to reported phasic firing, all tSPNs fire repetitively and linearly encode injected current magnitude to firing frequency over a broad range. Modeling studies suggest microelectrode impalement injury accounts for differences in tSPN properties previously observed. Overall, intrinsic tSPN excitability plays a much greater role in the integration and maintenance of sympathetic output than previously thought." |
| 12. |
DBS of a multi-compartment model of subthalamic nucleus projection neurons (Miocinovic et al. 2006)
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We built a comprehensive computational model of subthalamic nucleus (STN) deep brain stimulation (DBS) in parkinsonian macaques to study the effects of stimulation in a controlled environment. The model consisted of three fundamental components: 1) a three-dimensional (3D) anatomical model of the macaque basal ganglia, 2) a finite element model of the DBS electrode and electric field transmitted to the tissue medium, and 3) multicompartment biophysical models of STN projection neurons, GPi fibers of passage, and internal capsule fibers of passage. Populations of neurons were positioned within the 3D anatomical model. Neurons were stimulated with electrode positions and stimulation parameters defined as clinically effective in two parkinsonian monkeys. The model predicted axonal activation of STN neurons and GPi fibers during STN DBS. Model predictions regarding the degree of GPi fiber activation matched well with experimental recordings in both monkeys. |
| 13. |
DCN fusiform cell (Ceballos et al. 2016)
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Dorsal cochlear nucleus principal neurons, fusiform neurons, display heterogeneous spontaneous action potential activity and thus represent an appropriate model to study the role of different conductances in establishing firing heterogeneity. Particularly, fusiform neurons are divided into quiet, with no spontaneous firing, or active neurons, presenting spontaneous, regular firing. These modes are determined by the expression levels of an intrinsic membrane conductance, an inwardly rectifying potassium current (IKir). We used a computational model to test whether other subthreshold conductances vary homeostatically to maintain membrane excitability constant across the two subtypes. We found that Ih expression covaries specifically with IKir in order to maintain membrane resistance constant. The impact of Ih on membrane resistance is dependent on the level of IKir expression, being much smaller in quiet neurons with bigger IKir, but Ih variations are not relevant for creating the quiet and active phenotypes. We conclude that in fusiform neurons the variations of their different subthreshold conductances are limited to specific conductances in order to create firing heterogeneity and maintain membrane homeostasis. |
| 14. |
Dendritic action potentials and computation in human layer 2/3 cortical neurons (Gidon et al 2020)
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This code reproduces figs 3 and S9 in Dendritic action potentials in layer 2/3 pyramidal neurons of the human neocortex.
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| 15. |
Electrodiffusive astrocytic and extracellular ion concentration dynamics model (Halnes et al. 2013)
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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. |
| 16. |
Fractional leaky integrate-and-fire model (Teka et al. 2014)
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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. |
| 17. |
Function and energy constrain neuronal biophysics in coincidence detection (Remme et al 2018)
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" ... 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." |
| 18. |
HH model neuron of the Suprachiasmatic Nucleus including a persistent Na+ channel (Paul et al 2016)
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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. |
| 19. |
HH model of SCN neurons including a transient K+ channel (Bano-Otalora et al 2021)
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This MATLAB code is associated with the paper "Daily electrical activity in the master circadian clock of a diurnal mammal" by Beatriz Bano-Otalora, Matthew J Moye, Timothy Brown, Robert J Lucas, Casey O Diekman, Mino DC Belle. eLife 2021; 10:e68719
DOI: https://doi.org/10.7554/eLife.68179
It simulates a Hodgkin-Huxley-type model of the electrical activity of suprachiasmatic nucleus (SCN) neurons in the diurnal rodent Rhabdomys pumilio. Model parameters were inferred from current-clamp recordings using data assimilation (DA) algorithms available at https://github.com/mattmoye/neuroDA |
| 20. |
Hippocampal CA1 microcircuit model including somatic and dendritic inhibition
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Here, we investigate the role of (dis)inhibition on the lateral entorhinal cortex (LEC) induced dendritic spikes on hippocampal CA1 pyramidal cells. The circuit model consists of pyramidal, SST+, CCK+, CR+/VIP+, and CCK+/VIP+ cells. |
| 21. |
Hodgkin-Huxley with dynamic ion concentrations (Hubel and Dahlem, 2014)
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The classical Hodgkin--Huxley (HH) model neglects the time-dependence of ion concentrations in spiking dynamics. The dynamics is therefore limited to a time scale of milliseconds, which is determined by the membrane capacitance multiplied by the resistance of the ion channels, and by the gating time constants. This model includes slow dynamics in an extended HH framework that simulates time-dependent ion concentrations, pumps, and buffers. Fluxes across the neuronal membrane change intra- and extracellular ion concentrations, whereby the latter can also change through contact to reservoirs in the surroundings. The dynamics on three distinct slow times scales is determined by the cell volume-to-surface-area ratio and the membrane permeability (seconds), the buffer time constants (tens of seconds), and the slower backward buffering (minutes to hours). The modulatory dynamics and the newly emerging excitable dynamics corresponds to pathological conditions observed in epileptiform burst activity, and spreading depression in migraine aura and stroke, respectively. |
| 22. |
Initiation of spreading depolarization by GABAergic neuron hyperactivity & NaV 1.1 (Chever et al 21)
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Experimentally, we show that acute pharmacological activation of NaV1.1 (the main Na+ channel of interneurons) or optogenetic-induced hyperactivity of GABAergic interneurons is sufficient to ignite CSD in the neocortex by spiking-generated extracellular K+ build-up. Neither GABAergic nor glutamatergic synaptic transmission were required for CSD initiation. CSD was not generated in other brain areas, suggesting that this is a neocortex-specific mechanism of CSD initiation. Gain-of-function mutations of NaV1.1 (SCN1A) cause Familial Hemiplegic Migraine type-3 (FHM3), a subtype of migraine with aura, of which CSD is the neurophysiological correlate. Our results provide the mechanism linking NaV1.1 gain-of-function to CSD generation in FHM3.
Those findings are supported by the two-neuron conductance-based model with dynamic ion concentrations we developed. |
| 23. |
Intrinsic sensory neurons of the gut (Chambers et al. 2014)
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A conductance base model of intrinsic neurons neurons in the gastrointestinal tract. The model contains all the major voltage-gated and calcium-gated currents observed in these neurons. This model can reproduce physiological observations such as the response to multiple brief depolarizing currents, prolonged depolarizing currents and hyperpolarizing currents. This model can be used to predict how different currents influence the excitability of intrinsic sensory neurons in the gut. |
| 24. |
L5 pyr. cell spiking control by oscillatory inhibition in distal apical dendrites (Li et al 2013)
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This model examined how distal oscillatory inhibition influences the firing of a biophysically-detailed layer 5 pyramidal neuron model. |
| 25. |
Leaky Integrate and Fire Neuron Model of Context Integration (Calvin, Redish 2021)
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The maintenance of the contextual information has been shown to be sensitive to changes in excitation-inhbition (EI) balance. We constructed a multi-structure, biophysically-realistic agent that could perform context-integration as is assessed by the dot probe expectancy task. The agent included a perceptual network, a working memory network, and a decision making system and was capable of successfully performing the dot probe expectancy task. Systemic manipulation of the agent’s EI balance produced localized dysfunction of the memory structure, which resulted in schizophrenia-like deficits at context integration. |
| 26. |
Leech Heart (HE) Motor Neuron conductances contributions to NN activity (Lamb & Calabrese 2013)
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"...
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.
" |
| 27. |
Mouse Episodic and Continuous Locomotion CPG (Sharples et al, 2022)
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We modeled the locomotion CPG in neonatal mice, and our model produces both episodic and continuous locomotion, similar to activity seen in experimental recordings of motor neurons in the isolated spinal cords of neonatal mice. Our model is presented in Sharples SA, Parker J, Cruz JM, Vargas A, Lognon AP, Cheng N, Young L, Shonak A, Cymbalyuk G, Whelan PJ. Mechanisms of Episodic Rhythmicity Contributions of h-and Na+/K+ pump currents to the generation of episodic and continuous rhythmic activities. 2021. Frontiers in Cellular Neuroscience. 579. Accepted. |
| 28. |
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." |
| 29. |
Nodes of Ranvier with left-shifted Nav channels (Boucher et al. 2012)
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The two programs CLSRanvier.f and propagation.f simulate the excitability of a myelinated axon with injured nodes of Ranvier.
The injury is simulated as the Coupled Left Shift (CLS) of the activation(V) and inactivation(V) (availability) of a fraction of Nav channels. |
| 30. |
Peripheral nerve:Morris-Lecar implementation of (Schwarz et al 1995)
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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. |
| 31. |
Simulated cortical color opponent receptive fields self-organize via STDP (Eguchi et al., 2014)
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"...
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.
..." |
| 32. |
Single compartment Dorsal Lateral Medium Spiny Neuron w/ NMDA and AMPA (Biddell and Johnson 2013)
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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. |
| 33. |
Single E-I oscillating network with amplitude modulation (Avella Gonzalez et al. 2012)
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"... 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.
..."
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| 34. |
Single neuron with ion concentrations to model anoxic depolarization (Zandt et al. 2011)
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A minimal single neuron model, including changing ion concentrations and homeostasis mechanisms. It shows the sudden depolarization that occurs after prolonged anoxia/ischemia. |
| 35. |
Single-cell comprehensive biophysical model of SN pars compacta (Muddapu & Chakravarthy 2021)
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Parkinson’s disease (PD) is caused by the loss of dopaminergic cells in substantia nigra pars compacta (SNc), the decisive cause of this inexorable cell loss is not clearly elucidated. We hypothesize that “Energy deficiency at a sub-cellular/cellular/systems-level can be a common underlying cause for SNc cell loss in PD.” Here, we propose a comprehensive computational model of SNc cell which helps us to understand the pathophysiology of neurodegeneration at subcellular-level in PD. We were able to show see how deficits in supply of energy substrates (glucose and oxygen) lead to a deficit in ATP, and furthermore, deficits in ATP are the common factor underlying the pathological molecular-level changes including alpha-synuclein aggregation, ROS formation, calcium elevation, and dopamine dysfunction. The model also suggests that hypoglycemia plays a more crucial role in leading to ATP deficits than hypoxia. We believe that the proposed model provides an integrated modelling framework to understand the neurodegenerative processes underlying PD. |
| 36. |
Spreading Depolarization in Brain Slices (Kelley et al. 2022)
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A tissue-scale model of spreading depolarization (SD) in brain slices.
We used the NEURON simulator's reaction-diffusion framework to implement embed thousands of neurons
(based on the the model from Wei et al. 2014)
in the extracellular space of a brain slice, which is itself embedded in an bath solution.
We initiate SD in the slice by elevating extracellular K+ in a spherical region at the center of the slice.
Effects of hypoxia and propionate on the slice were modeled by appropriate changes to the volume fraction
and tortuosity of the extracellular space and oxygen/chloride concentrations. |
| 37. |
Spreading depression model for FHM3 with Nav1.1 mutation (Dahlem et al. 2014)
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Familial hemiplegic migraine (FHM) is a rare subtype of migraine with aura. A mutation causing FHM type 3 (FHM3) has been identified in SCN1A encoding the Nav1.1 Na+ channel. This genetic defect affects the inactivation gate. The code describes an extended Hodgkin-Huxley framework with dynamic ion concentrations in a wilde-type and mutant form.
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| 38. |
Stochastic Hodgkin-Huxley Model: 14x28D Langevin Simulation (Pu and Thomas, 2020).
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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)
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| 39. |
Subiculum network model with dynamic chloride/potassium homeostasis (Buchin et al 2016)
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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. |
| 40. |
The electrodiffusive neuron-extracellular-glia (edNEG) model (Sætra et al. 2021)
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"... We here present the electrodiffusive neuron-extracellular-glia (edNEG) model, which we believe is the first model to combine compartmental neuron modeling with an electrodiffusive framework for intra- and extracellular ion concentration dynamics in a local piece of neuro-glial brain tissue. The edNEG model (i) keeps track of all intraneuronal, intraglial, and extracellular ion concentrations and electrical potentials, (ii) accounts for action potentials and dendritic calcium spikes in neurons, (iii) contains a neuronal and glial homeostatic machinery that gives physiologically realistic ion concentration dynamics, (iv) accounts for electrodiffusive transmembrane, intracellular, and extracellular ionic movements, and (v) accounts for glial and neuronal swelling caused by osmotic transmembrane pressure gradients. The edNEG model accounts for the concentration-dependent effects on ECS potentials that the standard models neglect. Using the edNEG model, we analyze these effects by splitting the extracellular potential into three components: one due to neural sink/source configurations, one due to glial sink/source configurations, and one due to extracellular diffusive currents ..." |
| 41. |
The electrodiffusive Pinsky-Rinzel (edPR) model (Sætra et al., 2020)
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The edPR model is "what we may refer to as “a minimal neuronal model that
has it all”. By “has it all”, we mean that it (1) has a spatial extension, (2) considers both extracellular- and
intracellular dynamics, (3) keeps track of all ion concentrations (Na+, K+, Ca2+, and
Cl-) in all compartments, (4) keeps track of all electrical potentials in all compartments,
(5) has differential expression of ion channels in soma versus dendrites,
and can fire somatic APs and dendritic calcium spikes,
(6) contains the homeostatic machinery that ensures that it maintains a realistic dynamics in the membrane potential
and all ion concentrations during long-time activity, and (7) accounts for transmembrane,
intracellular and extracellular ionic movements due to both diffusion and electrical migration,
and thus ensures a consistent relationship between ion concentrations and electrical charge.
Being based on a unified framework for intra- and extracellular dynamics, the model
thus accounts for possible ephaptic effects from extracellular dynamics, as neglected in
standard feedforward models based on volume conductor theory. By “minimal”
we simply mean that we reduce the number of spatial compartments to the minimal, which in
this case is four, i.e., two neuronal compartments (a soma and a dendrite), plus two extracellular
compartments (outside soma and outside dendrite). Technically, the model was
constructed by adding homeostatic mechanisms and ion concentration dynamics to an existing
model, i.e., the two-compartment Pinsky-Rinzel (PR) model, and embedding in it a
consistent electrodiffusive framework, i.e., the previously developed Kirchhoff-Nernst-Planck framework." |
| 42. |
The role of glutamate in neuronal ion homeostasis: spreading depolarization (Hubel et al 2017)
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This model includes ion concentration dynamics (sodium, potassium, chloride) inside and outside the neuron, the exchange of ions with glia and blood vessels, volume dynamics of neuron, glia, and extracellular space, glutamate homeostasis involving release by neuron and uptake by both neuron and glia. Spreading depolarization is used as a case study. |
| 43. |
The ventricular AP and effects of the isoproterenol-induced cardiac hypertrophy (Sengul et al 2020)
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This model reproduces Action Potential (AP) of Rat Ventricular Myocytes according to the experimental AP and Voltage Clamp recordings. |
| 44. |
Two-neuron conductance-based model with dynamic ion concentrations to study NaV1.1 channel mutations
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Gain of function mutations of SCN1A, the gene coding for the voltage-gated sodium channel NaV1.1, cause familial hemiplegic migraine type 3 (FHM-3), whereas loss of function mutations cause different types of epilepsy.
To study those mutations, we developed a two-neuron conductance-based model of interconnected GABAergic and pyramidal glutamatergic neurons, with dynamic ion concentrations. We modeled FHM-3 mutations with persistent sodium current in the GABAergic neuron and epileptogenic mutations by decreasing the fast-inactivating sodium conductance in the GABAergic neuron. |
| 45. |
VTA dopamine neuron (Tarfa, Evans, and Khaliq 2017)
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In our model of a midbrain VTA dopamine neuron, we show that the decay kinetics of the A-type potassium current can control the timing of rebound action potentials. |
