A model of stimulus-specific adaptation in neuromorphic a VLSI

Abstract

Stimulus-specific adaptation (SSA) is a phenomenon observed in neural systems which occurs when the spike count elicited in a single neuron by external stimuli decreases with repetitions of the same stimulus, and recovers when a different stimulus is presented. SSA therefore effectively highlights rare events in stimulus sequences, and suppresses responses to repetitive ones. In this paper we present a model of SSA based on synaptic depression and describe its implementation in neuromorphic analog VLSI. The hardware system is evaluated using biologically realistic spike trains with parameters chosen to match those used in physiological experiments. We examine the effect of input parameters upon SSA and show that the trends apparent in the results obtained in silico compare favourably with those observed in biological neurons.