A Bayesian constraint on neural computation

Discovery of optimizations in biology provides a way to infer evolved function. Such optimizations are being sought at the level of neurons and synapses. Constraints being considered include information rates, metabolic costs, and time. Here we point out a classic optimization: neural computation should be Bayesian with a known prior distribution. A particular form of Bayesian inference is biologically feasible and necessarily linear. However, the linear requirement engenders metabolic costs, which are illustrated here.