  The sensory-triggered activity of a neuron is typically characterized in
terms of a tuning curve, which describes the neuron's average response as a
function of a parameter that characterizes a physical stimulus. What determines
the shapes of tuning curves in a neuronal population? Previous theoretical
studies and related experiments suggest that many response characteristics of
sensory neurons are optimal for encoding stimulus-related information. This
notion, however, does not explain the two general types of tuning profiles that
are commonly observed: unimodal and monotonic. Here, I quantify the efficacy of
a set of tuning curves according to the possible downstream motor responses
that can be constructed from them. Curves that are optimal in this sense may
have monotonic or non-monotonic profiles, where the proportion of monotonic
curves and the optimal tuning curve width depend on the general properties of
the target downstream functions. This dependence explains intriguing features
of visual cells that are sensitive to binocular disparity and of neurons tuned
to echo delay in bats. The numerical results suggest that optimal sensory
tuning curves are shaped not only by stimulus statistics and signal-to-noise
properties, but also according to their impact on downstream neural circuits
and, ultimately, on behavior.
