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Anton V. Chizhov

Researcher at Ioffe Institute

Publications -  54
Citations -  730

Anton V. Chizhov is an academic researcher from Ioffe Institute. The author has contributed to research in topics: Population & Excitatory postsynaptic potential. The author has an hindex of 15, co-authored 52 publications receiving 593 citations. Previous affiliations of Anton V. Chizhov include Russian Academy of Sciences & Centre national de la recherche scientifique.

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Efficient evaluation of neuron populations receiving colored-noise current based on a refractory density method

TL;DR: The expected firing probability of a stochastic neuron is approximated by a function of the expected subthreshold membrane potential, for the case of colored noise, in order to extend the recently proposed white noise model to conductance-based neurons.
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Population model of hippocampal pyramidal neurons, linking a refractory density approach to conductance-based neurons.

TL;DR: A macroscopic approach toward realistic simulations of the population activity of hippocampal pyramidal neurons, based on the known refractory density equation with a different hazard function and on a different single-neuron threshold model is proposed.
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Reduced Efficacy of the KCC2 Cotransporter Promotes Epileptic Oscillations in a Subiculum Network Model.

TL;DR: Using a realistic neural network model describing ion mechanisms, it is shown that chloride homeostasis pathology provokes seizure activity analogous to recordings from epileptogenic brain tissue and predicts that restoration of the chlorideHomeostasis in pyramidal cells could be a viable antiepileptic strategy.
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Minimal model of interictal and ictal discharges “Epileptor-2”

TL;DR: A minimal population-type model of IIDs and IDs is proposed that is as simple to use as the Epileptor but the suggested model attributes physical meaning to the variables, and may serve as a hierarchical base from a simple to more complex modeling of seizures.
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Mind the last spike - firing rate models for mesoscopic populations of spiking neurons.

TL;DR: The refractory density method (RDM), also known as age-structured population dynamics or quasi-renewal theory, yields a powerful theoretical framework to build rate-based models for mesoscopic neural populations from realistic neuron dynamics at the microscopic level.