UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

On conductance-based neural field models.

Pinotsis, DA; Leite, M; Friston, KJ; (2013) On conductance-based neural field models. Front Comput Neurosci , 7 , Article 158. 10.3389/fncom.2013.00158. Green open access

[thumbnail of fncom-07-00158.pdf]
Preview
PDF
fncom-07-00158.pdf

Download (2MB)

Abstract

This technical note introduces a conductance-based neural field model that combines biologically realistic synaptic dynamics-based on transmembrane currents-with neural field equations, describing the propagation of spikes over the cortical surface. This model allows for fairly realistic inter-and intra-laminar intrinsic connections that underlie spatiotemporal neuronal dynamics. We focus on the response functions of expected neuronal states (such as depolarization) that generate observed electrophysiological signals (like LFP recordings and EEG). These response functions characterize the model's transfer functions and implicit spectral responses to (uncorrelated) input. Our main finding is that both the evoked responses (impulse response functions) and induced responses (transfer functions) show qualitative differences depending upon whether one uses a neural mass or field model. Furthermore, there are differences between the equivalent convolution and conductance models. Overall, all models reproduce a characteristic increase in frequency, when inhibition was increased by increasing the rate constants of inhibitory populations. However, convolution and conductance-based models showed qualitatively different changes in power, with convolution models showing decreases with increasing inhibition, while conductance models show the opposite effect. These differences suggest that conductance based field models may be important in empirical studies of cortical gain control or pharmacological manipulations.

Type: Article
Title: On conductance-based neural field models.
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fncom.2013.00158
Publisher version: http://dx.doi.org/10.3389/fncom.2013.00158
Additional information: © 2013 Pinotsis, Leite and Friston. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. PMCID: PMC3824089
Keywords: conductance based models, dynamic causal modeling, electrophysiology, mean field modeling, neural field theory
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Experimental Epilepsy
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/1413150
Downloads since deposit
105Downloads
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item