As animals interact with their environments, they must constantly
update estimates about their states. Bayesian models provide a
description of statistically optimal updating based on prior
probabilities, a dynamical model, and sensory evidence, and have
proved to be consistent with the results of many diverse
psychophysical studies. However, little is known about the neural
representation and manipulation of such Bayesian information,
particularly in populations of spiking neurons. We consider this
issue, suggesting an interpretation based on standard neural
architecture and activations. We illustrate the approach on a
simple spatial navigation example, and then apply it to a
sensorimotor integration task that provides a particularly
compelling example of dynamic probabilistic computation.