Context-dependent adaptation in a neural computation

Brains adapt to the statistical structure of their input. In the visual system, local light intensities change rapidly, the variance of the intensity changes more slowly, and the dynamic range of contrast itself changes more slowly still. We use a motion-sensitive neuron in the fly visual system to probe this hierarchy of adaptation phenomena, delivering naturalistic stimuli that have been simplified to have a clear separation of timescales. We show that the neural response to visual motion depends on contrast and that this dependence itself varies with context. Using the spike-triggered average velocity trajectory as a response measure, we find that this context dependence is confined to a low-dimensional space, dominated by a single principal dimension. To quantify the functional consequences of this structure, we measure the mutual information between stimulus and single-spike responses under models with and without adaptive structure. Across a wide range of conditions, incorporating adaptation consistently increases mutual information, indicating that context-dependent changes in the encoding model improve information transmission.