Abstract

A stochastic model for visual evoked response generation is proposed based on a compound neurological generator approach. Participation of individual generators is stochastically modelled in a physiologically realistic manner that captures the inherent variability in latencies and amplitudes associated with the component phases of the response. The model is invertible such that decomposition of real responses to reveal individual unit generator participation is possible and suggests that conventional averaging techniques may provide a truer picture of the visual evoked response than previously thought.