title: An analytical model for the "large, fluctuating synaptic conductance state" typical of neocortical neurons in vivo.
abstract: A model of in vivo-like neocortical activity is studied analytically in relation to experimental data and other models in order to understand the essential mechanisms underlying such activity. The model consists of a network of sparsely connected excitatory and inhibitory integrate-and-fire (IF) neurons with conductance-based synapses. It is shown that the model produces values for five quantities characterizing in vivo activity that are in agreement with both experimental ranges and a computer-simulated Hodgkin-Huxley model adapted from the literature (Destexhe et al. (2001) Neurosci. 107(1): 13-24). The analytical model builds on a study by Brunel (2000) (J. Comput. Neurosci. 8: 183-208), which used IF neurons with current-based synapses, and therefore does not account for the full range of experimental data. The present results suggest that the essential mechanism required to explain a range of data on in vivo neocortical activity is the conductance-based synapse and that the particular model of spike initiation used is not crucial. Thus the IF model with conductance-based synapses may provide a basis for the analytical study of the "large, fluctuating synaptic conductance state" typical of neocortical neurons in vivo.
authors.affiliation: The Bionic Ear Institute, 384-388 Albert St, East Melbourne, Victoria 3002, Australia. h.meffin@medoto.unimelb.edu.au
authors.forename: Hamish
authors.initials: H
authors.lastname: Meffin
authors.forename: Anthony N
authors.initials: AN
authors.lastname: Burkitt
authors.forename: David B
authors.initials: DB
authors.lastname: Grayden
mesh_terms: D000200:Action Potentials / Q000502:physiology; D000818:Animals; D019706:Excitatory Postsynaptic Potentials / Q000502:physiology; D008959:Models, Neurological; D019579:Neocortex / Q000166:cytology* / Q000502:physiology; D009431:Neural Conduction / Q000502:physiology*; D009433:Neural Inhibition / Q000502:physiology; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology*; D013569:Synapses / Q000502:physiology*; D009435:Synaptic Transmission / Q000502:physiology