title: On conductance-based neural field models.
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.
authors.affiliation: The Wellcome Trust Centre for Neuroimaging, University College London London, UK.
authors.forename: Dimitris A
authors.initials: DA
authors.lastname: Pinotsis
authors.forename: Marco
authors.initials: M
authors.lastname: Leite
authors.forename: Karl J
authors.initials: KJ
authors.lastname: Friston
keywords: conductance based models; dynamic causal modeling; electrophysiology; mean field modeling; neural field theory