Simulated generation of evoked potentials components using networks with distinct excitatory and inhibitory neurons

Long latency evoked potentials (EP's) are electrical potentials related to brain information processing mechanisms. In this paper, three-layered neurophysiologically based artificial neural network model is presented whose neurons obey to Dale's law. The first two layers of the network can memorize and recall sparsely coded patterns, oscillating at biologically plausible frequencies. Excitatory low-pass filtering synapses, from the second to the third layer, create evoked current dipoles, when the network retrieves memories related to stimuli. Based on psychophysiological indications, simulated intracranial dipoles are straightforwardly transformed into long latency EP components such as N100 and P300 that match laboratory-measured scalp EP's.