Speaker
Description
Sensory systems continuously adapt their responses based on the statistics of the environment. Recording in mouse V1 during stimulus sequences sampled from different statistical distributions, we found that the average population response follows a power law of stimulus probability, with an exponent invariant across environments for a given stimulus type. An efficient coding model trading representational fidelity against energy cost reproduced this power law and explained its invariance, whereas alternative coding objectives did not. We also found a shallower exponent for natural stimuli than gratings; the model explains this by more separated representations affording discriminability without costly gain modulation. In separate recordings, we then asked how adaptation modifies the representations that relate more directly to perception. Surprisingly, discriminability increased between more frequent stimuli, even as responses to those stimuli decreased, an effect arising from the geometry of the mean population responses rather than from the noise structure, and reproduced in artificial networks trained to reconstruct stimuli under metabolic constraints. Adaptation thus reorganizes representational geometry to encode the visual environment under metabolic constraints.