Simplest relationship between local field potential and intracellular signals in layered neural tissue

Chizhov, Anton V.; Sánchez-Aguilera, Alberto; Rodrigues, Serafim; Prida, Liset Menéndez de la

doi:10.1103/physreve.92.062704

Cited by 17 publications

(22 citation statements)

References 39 publications

(71 reference statements)

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“…Basic neurons have 2-compartments, according to [ 32 ], with the somatic and dendritic voltages U E ( t , t *) and . In comparison with the one-compartment model, the extra parameters is the ratio of dendritic to somatic conductances γ and the dendritic length.…”

Section: Methodsmentioning

confidence: 99%

“…In spite of different locations of the synapses, both excitatory and inhibitory conductances are attributed to the somatic compartment, to be compared with experimental whole-cell somatic registrations. For that purpose, the model from [ 32 ] is constructed such that it implicitly solves a reverse voltage-clamp problem and thus estimates the dendritic synaptic current. The implementation of the 2-compartment model in the CBRD-model is straightforward, it increases the number of equations but not the number of independent variables.…”

Section: Methodsmentioning

confidence: 99%

“…Parameterized by t *, the governing equations are as follows: where is the total conductance; l is the square ratio of the dendritic length to the characteristic length. The somatic and dendritic synaptic currents I soma and I dendr are calculated as where the differential operator represents the solution of the reverse problem of dendritic current estimation from somatically registered-like conductances [ 32 ]. We imply that the synaptic conductance kinetics is estimated from somatic responses to stimulation of presynaptic neuronal population.…”

Section: Methodsmentioning

confidence: 99%

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Computational model of interictal discharges triggered by interneurons

2017

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Interictal discharges (IIDs) are abnormal waveforms registered in the periods before or between seizures. IIDs that are initiated by GABAergic interneurons have not been mathematically modeled yet. In the present study, a mathematical model that describes the mechanisms of these discharges is proposed. The model is based on the experimental recordings of IIDs in pyramidal neurons of the rat entorhinal cortex and estimations of synaptic conductances during IIDs. IIDs were induced in cortico-hippocampal slices by applying an extracellular solution with 4-aminopyridine, high potassium, and low magnesium concentrations. Two different types of IIDs initiated by interneurons were observed. The first type of IID (IID1) was pure GABAergic. The second type of IID (IID2) was induced by GABAergic excitation and maintained by recurrent interactions of both GABA- and glutamatergic neuronal populations. The model employed the conductance-based refractory density (CBRD) approach, which accurately approximates the firing rate of a population of similar Hodgkin-Huxley-like neurons. The model of coupled excitatory and inhibitory populations includes AMPA, NMDA, and GABA-receptor-mediated synapses and gap junctions. These neurons receive both arbitrary deterministic input and individual colored Gaussian noise. Both types of IIDs were successfully reproduced in the model by setting two different depolarized levels for GABA-mediated current reversal potential. It was revealed that short-term synaptic depression is a crucial factor in ceasing each of the discharges, and it also determines their durations and frequencies.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Computational model of interictal discharges triggered by interneurons

2017

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“…Given the distant-dependence of the LFP signal, this model should be considered only as a first approximation (Buzsáki et al, 2012). More detailed approaches describing populations of two-compartmental neurons (Chizhov, 2013, Chizhov et al, 2015 could also provide better approximation for the LFP.…”

Section: Discussionmentioning

confidence: 99%

Adaptation and inhibition control pathological synchronization in a model of focal epileptic seizure

Buchin

Kerr

Huberfeld

et al. 2018

Preprint

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Pharmacoresistant epilepsy is a common neurological disorder in which increased neuronal intrinsic excitability and synaptic excitation lead to pathologically synchronous behavior in the brain. In the majority of experimental and theoretical epilepsy models, epilepsy is associated with reduced inhibition in the pathological neural circuits, yet effects of intrinsic excitability are usually not explicitly analyzed. Here we present a novel neural mass model that includes intrinsic excitability in the form of spike-frequency adaptation in the excitatory population. We validated our model using local field potential data recorded from human hippocampal/subicular slices. We found that synaptic conductances and slow adaptation in the excitatory population both play essential roles for generating seizures and pre-ictal oscillations. Using bifurcation analysis, we found that transitions towards seizure and back to the resting state take place via Andronov-Hopf bifurcations. These simulations therefore suggest that single neuron adaptation as well as synaptic inhibition are responsible for orchestrating seizure dynamics and transition towards the epileptic state.Significance statementEpileptic seizures are commonly thought to arise from a pathology of inhibition in the brain circuits. Theoretical models aiming to explain epileptic oscillations usually describe the neural activity solely in terms of inhibition and excitation. Single neuron adaptation properties are usually assumed to have only a limited contribution to seizure dynamics. To explore this issue, we developed a novel neural mass model with adaption in the excitatory population. By including adaptation and intrinsic excitability together with inhibition in this model, we were able to account for several experimentally observed properties of seizures, resting state dynamics, and pre-ictal oscillations, leading to improved understanding of epileptic seizures.

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“…2C,D). One of the advantages of the approach is its applicability to multi-compartment neurons, which is crucial for accurately matching simulations to experiments and for calculating local field potentials, both requiring two compartments [65]. Simulations of the visual cortex activity and epileptic discharges matched a set of data obtained with patch-clamp recordings and optical imaging [66 • , 67 • , 50, 55].…”

Section: The Refractory Density Approachmentioning

confidence: 99%

Mind the last spike — firing rate models for mesoscopic populations of spiking neurons

Schwalger

Chizhov

2019

Current Opinion in Neurobiology

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Highlights• Generalized integrate-and-fire (GIF) models permit efficient extraction of point neuron parameters, which reproduce the spiking behavior of multiple cell types and are available from a public database.• Populations of GIF or conductance-based neuron models can be described by a single state variable -the "time since the last spike" -enabling efficient refractory density methods.• Refractory density model accurately captures transient dynamics and finite-size fluctuations of mesoscopic activities of spiking neuron populations.• Next-generation firing-rate models for finite-size populations of spiking neurons arise from a low-dimensional reduction of the refractory density equation.1 AbstractThe dominant modeling framework for understanding cortical computations are heuristic firing rate models. Despite their success, these models fall short to capture spike synchronization effects, to link to biophysical parameters and to describe finite-size fluctuations. In this opinion article, we propose that 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. We review recent advances achieved by the RDM to obtain efficient population density equations for networks of generalized integrate-and-fire (GIF) neurons -a class of neuron models that has been successfully fitted to various cell types. The theory not only predicts the nonstationary dynamics of large populations of neurons but also permits an extension to finite-size populations and a systematic reduction to low-dimensional rate dynamics. The new types of rate models will allow a re-examination of models of cortical computations under biological constraints.

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Simplest relationship between local field potential and intracellular signals in layered neural tissue

Cited by 17 publications

References 39 publications

Computational model of interictal discharges triggered by interneurons

Computational model of interictal discharges triggered by interneurons

Adaptation and inhibition control pathological synchronization in a model of focal epileptic seizure

Mind the last spike — firing rate models for mesoscopic populations of spiking neurons

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