Stimulus-specific adaptation (SSA) is a phenomenon observed in neural systems which occurs when the spike count elicited in a single neuron 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 very-large-scale integration (VLSI). The hardware system is evaluated using biologically realistic spike trains with parameters chosen to reflect those of the stimuli used in physiological experiments. We examine the effect of input parameters and stimulus history upon SSA and show that the trends apparent in the results obtained in silico compare favorably with those observed in biological neurons.