Dynamic Adaptation in Fly Motion Vision

Sensory neurons process and convey information about our surroundings, providing the physiological basis for how we interact with the external world. In order to understand neuronal responses we must identify the rules governing how sensory information is encoded. It was proposed more than fifty years ago that neural codes constitute efficient representations of the natural world (Attneave, 1954; Barlow, 1961). In an information maximization paradigm, an efficient coding strategy will match the encoded neural response to the statistics of the input signals. Adaptation of the stimulus-response function to the statistics of the stimulus is one way to efficiently encode a stimulus when the response range and resolution are limited compared to the entire range of stimulus probabilities (Laughlin, 1981). Recent work has indeed shown that adaptation to the input statistics can occur in real time (Smirnakis et al., 1997) and that this form of adaptation can be used to efficiently encode the stimulus and maximize information transmission (Brenner et al., 2000). Abstract In this work I examined the mechanisms of dynamic adaptation in fly motion vision. The H1cell is a large field tangential cell of the blowfly visual system that responds to motion in a directionally selective way. It also adapts its response properties to the second order statistics of an apparent motion stimulus (Fairhall et al., 2001). I measured the adaptation of the H1cell to the variance and temporal correlations of a Gaussian low-pass filtered velocity signal that directed a sine wave visual grating. I found that the H1-cell adapted the slope, or gain, and range of its input-output function to the variance of the velocity signal over two orders of magnitude. The H1-cell also adapted its response properties to the low-pass filter time constant of the velocity signal over one order of magnitude. I compared the adaptation between flies by normalizing the gain of the stimulus-response function by the gain of the stimulus-response function during steady-state firing properties. This “dynamic gain” decreased as the velocity variance increased and broadened to cover the larger range of velocities. In contrast, as the time constant of the velocity fluctuations increased, the dynamic gain increased. The results of these experiments were then compared with simulations of the correlation-type or Reichardt motion detector model. The Reichardt detector is an algorithmic model for