  Biological systems display impressive capabilities in effectively responding
to environmental signals in real time. There is increasing evidence that
organisms may indeed be employing near optimal Bayesian calculations in their
decision-making. An intriguing question relates to the properties of optimal
encoding methods, namely determining the properties of neural populations in
sensory layers that optimize performance, subject to physiological constraints.
Within an ecological theory of neural encoding/decoding, we show that optimal
Bayesian performance requires neural adaptation which reflects environmental
changes. Specifically, we predict that neuronal tuning functions possess an
optimal width, which increases with prior uncertainty and environmental noise,
and decreases with the decoding time window. Furthermore, even for static
stimuli, we demonstrate that dynamic sensory tuning functions, acting at
relatively short time scales, lead to improved performance. Interestingly, the
narrowing of tuning functions as a function of time was recently observed in
several biological systems. Such results set the stage for a functional theory
which may explain the high reliability of sensory systems, and the utility of
neuronal adaptation occurring at multiple time scales.
