  We characterize the computation of motion in the fly visual system as a
mapping from the high dimensional space of signals in the retinal photodetector
array to the probability of generating an action potential in a motion
sensitive neuron. Our approach to this problem identifies a low dimensional
subspace of signals within which the neuron is most sensitive, and then samples
this subspace to visualize the nonlinear structure of the mapping. The results
illustrate the computational strategies predicted for a system that makes
optimal motion estimates given the physical noise sources in the detector
array. More generally, the hypothesis that neurons are sensitive to low
dimensional subspaces of their inputs formalizes the intuitive notion of
feature selectivity and suggests a strategy for characterizing the neural
processing of complex, naturalistic sensory inputs.
