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).
In this work I examined the mechanisms of dynamic adaptation in fly motion vision. The H1-cell 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 H1-cell 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 motion detection that explains the behavior of directionally selective large-field tangential cells in flies including the H1-cell, as well as directionally selective motion vision in humans (Zanker, 1996; Borst and Egelhaaf, 1989). The Reichardt detector model showed the same adaptive properties as the H1-cell in response to the same stimuli. Reichardt detector adaptation occurred without changing any of the model parameters; it was an automatic function of the dynamics of the model. This suggested that the mathematical properties of the Reichardt detector provide a mechanism for adaptation in the H1-cell of the blowfly.
This adaptation was further characterized in both the Reichardt detector model and the H1-cell. The time course of this form of velocity adaptation in the H1-cell was examined by switching between two different variances and two different low-pass filter time constants of the velocity signal. The H1-cell adapted to the statistics or the time course of the new velocity signal within two seconds after the switch. The Reichardt detector showed a similar time course for adaptation as in the experiments. The effect of the visual pattern on adaptation was also examined, using a square wave pattern in addition to the sine wave used previously. The visual pattern affects the output of an array of Reichardt motion detectors and may therefore affect adaptation in the system. The overall shape of the adaptation function with respect to the stimulus variance was not different between the two stimulus patterns. In the experiments, the H1-cell showed a consistently higher dynamic gain with a square wave pattern. The Reichardt detector model, however, had a lower dynamic gain when the square wave pattern was presented. After careful investigation of the potential causes of this discrepancy I found that the steady-state firing rate of the H1-cell saturated when a square wave pattern was used, thereby altering the normalization under experimental conditions that was not accounted for in the simulations. These results suggest that contrast saturation is an important feature of fly motion vision that has not been explained by the Reichardt detector model.
The Reichardt detector provides an automatic mechanism and mathematical explanation for adaptation in the fly visual system involving the nature of the incoming visual signals and the non-linearity in the motion detector model. Interestingly, the gradient detector model, although it is also non-linear, does not display automatic adaptation. It remains to be seen whether this type of adaptation is prominent in other sensory systems and whether it leads to and efficient and accurate representation of the natural world
