Michael DeWeese & Anthony Zador
La Jolla, CA 92037
While it has long been recognized that sensory systems adapt to their inputs, the dynamics of the adaptation has received less consideration. Here we formulate the problem of optimal variance estimation for a broad class of non-stationary signals. We find that, under weak assumptions, the Bayesian optimal causal variance estimate shows asymmetric dynamics: An abrupt increase in variance is more readily detectable than an abrupt decrease. This observation makes specific and falsifiable predictions about the time course of adaptation in neurons probed with certain stimulus ensembles.