A large-scale simulation of the piriform cortex by a cell automaton-based network model.

Abstract

An event-driven framework is used to construct a physiologically motivated large-scale model of the piriform cortex containing in the order of 10(5) neuron-like computing units. This approach is based on a hierarchically defined highly abstract neuron model consisting of finite-state machines. It provides computational efficiency while incorporating components which have identifiable counterparts in the neurophysiological domain. The network model incorporates four neuron types, and glutamatergic excitatory and GABA(A) and GABA(B) inhibitory synapses. The spatio-temporal patterns of cortical activity and the temporal and spectral characteristics of simulated electroencephalograms (EEGs) are studied. In line with previous experimental and compartmental work, 1) shock stimuli elicit EEG profiles with either isolated peaks or damped oscillations, the response type being determined by the intensity of the stimuli, and 2) temporally unpatterned input generates EEG oscillations supported by model-wide waves of excitation.