Models that contain the Current : I Cl, leak

(Chloride leak channel)
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    Models   Description
1.  A model for recurrent spreading depolarizations (Conte et al. 2017)
A detailed biophysical model for a neuron/astrocyte network is developed in order to explore mechanisms responsible for cortical spreading depolarizations. This includes a model for the Na+-glutamate transporter, which allows for a detailed description of reverse glutamate uptake. In particular, we consider the specific roles of elevated extracellular glutamate and K+ in the initiation, propagation and recurrence of spreading depolarizations.
2.  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.
3.  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.
4.  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.
5.  Alcohol action in a detailed Purkinje neuron model and an efficient simplified model (Forrest 2015)
" ... 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. ..."
6.  An ion-based model for swelling of neurons and astrocytes (Hubel & Ullah 2016)
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.
7.  Anoxic depolarization, recovery: effect of brain regions and extracellular space (Hubel et al. 2016)
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.
8.  Basal ganglia-thalamic network model for deep brain stimulation (So et al. 2011)
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.
9.  ClC-2 channels regulate neuronal excitability, not intracellular Cl- levels (Ratte & Prescott 2011)
"The model is for a generic, single compartment neuron with multiple ion currents. The most notable mechanisms include ClC-2 (a rectifying chloride-leak channel) and KCC2 (potassium chloride co-transporter 2). A significant feature of the model is that it tracks intracellular chloride concentration. Moreover, the GABA-A receptor is modeled as passing both chloride and bicarbonate ions, which is important for proper calculation of the GABA reversal potential. Ornstein-Unlenbeck processes to simulate synaptic inhibition and excitation are also included."
10.  Concentration dependent nonlinear K+ and Cl- leak current (Huang et al. 2015)
"In their seminal works on squid giant axons, Hodgkin, and Huxley approximated the membrane leak current as Ohmic, i.e., linear, since in their preparation, sub-threshold current rectification due to the influence of ionic concentration is negligible. Most studies on mammalian neurons have made the same, largely untested, assumption. Here we show that the membrane time constant and input resistance of mammalian neurons (when other major voltage-sensitive and ligand-gated ionic currents are discounted) varies non-linearly with membrane voltage, following the prediction of a Goldman-Hodgkin-Katz-based passive membrane model. ..." (see paper for details and more).
11.  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.
12.  Dentate gyrus network model pattern separation and granule cell scaling in epilepsy (Yim et al 2015)
The dentate gyrus (DG) is thought to enable efficient hippocampal memory acquisition via pattern separation. With patterns defined as spatiotemporally distributed action potential sequences, the principal DG output neurons (granule cells, GCs), presumably sparsen and separate similar input patterns from the perforant path (PP). In electrophysiological experiments, we have demonstrated that during temporal lobe epilepsy (TLE), GCs downscale their excitability by transcriptional upregulation of ‘leak’ channels. Here we studied whether this cell type-specific intrinsic plasticity is in a position to homeostatically adjust DG network function. We modified an established conductance-based computer model of the DG network such that it realizes a spatiotemporal pattern separation task, and quantified its performance with and without the experimentally constrained leaky GC phenotype. ...
13.  Dopamine neuron of the vent. periaqu. gray and dors. raphe nucleus (vlPAG/DRN) (Dougalis et al 2017)
The following computer model describes the electrophysiological properties of dopamine (DA) neurons of the ventrolateral periaquaductal gray and dorsal raphe nucleus (vlPAG/DRN). the model and how to replicate Figures 7-10 of the manuscript (Dougalis et al., 2017 J Comput Neurosci). SUMMARY: We have conducted a voltage-clamp study to provide a kinetic description of major sodium, potassium and calcium ionic currents operant on adult DA vlPAG/DRN neurons in brain slices obtained from pitx3-GFP mice. Based on experimentally derived voltage-clamp data, we then constructed a simplified, conductance-based, Hodgkin and Huxley-type, computer model and validated its behaviour against in vitro neurophysiological data. Using simulations in the computational DA model, we explored the contribution of individual ionic currents in vlPAG/DRN DA neuron’s spontaneous firing, pacemaker frequency and threshold for spike frequency adaptation in silico. The data presented here extend our previous physiological characterization (Dougalis et al. 2012) and argue that DA neurons of the vlPAG/DRN express autorhythmicity in the absence of synaptic transmission via the interplay of potassium and sodium currents without the absolute need of calcium currents. The properties of the ionic currents recorded here (IH current, IA current), the lack of small oscillating potentials in the presence of sodium channel blockers taken together with the mechanisms for autorhythmicity (reliance more on sodium rather than calcium currents) also support further the idea that vlPAG/DRN DA neurons are operationally similar to VTA, rather than SNc, DA neurons. In particular, the properties of a slowly inactivating IA current in conjunction with the small and slowly activating IH current described herein pinpoint that vlPAG/DRN DA neurons are most similar to prefrontal cortex or medial shell of nucleus accumbens projecting DA neurons (see Lammel et al. 2008, 2011).
14.  Effects of increasing CREB on storage and recall processes in a CA1 network (Bianchi et al. 2014)
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.
15.  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.
16.  Endocannabinoid dynamics gate spike-timing dependent depression and potentiation (Cui et al 2016)
The endocannabinoid (eCB) system is considered involved in synaptic depression. Recent reports have also linked eCBs to synaptic potentiation. However it is not known how eCB signaling may support such bidirectionality. To question the mechanisms of this phenomena in spike-timing dependent plasticity (STDP) at corticostriatal synapses, we combined electrophysiology experiments with biophysical modeling. We demonstrate that STDP is controlled by eCB levels and dynamics: prolonged and moderate levels of eCB lead to eCB-mediated long-term depression (eCB-tLTD) while short and large eCB transients produce eCB-mediated long-term potentiation (eCB-tLTP). Therefore, just like neurotransmitters glutamate or GABA, eCB form a bidirectional system.
17.  Hodgkin-Huxley with dynamic ion concentrations (Hubel and Dahlem, 2014)
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.
18.  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. ... "
19.  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.
20.  Rhesus Monkey Young and Aged L3 PFC Pyramidal Neurons (Rumbell et al. 2016)
A stereotypical pyramidal neuron morphology with ion channel parameter combinations that reproduce firing patterns of one young and one aged rhesus monkey L3 PFC pyramidal neurons. Parameters were found through an automated optimization method.
21.  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.
22.  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
23.  Spreading depression model for FHM3 with Nav1.1 mutation (Dahlem et al. 2014)
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.
24.  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.
25.  The role of glutamate in neuronal ion homeostasis: spreading depolarization (Hubel et al 2017)
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.

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