Adaptation shapes the representational geometry in mouse V1 to efficiently encode the environment

Abstract

Sensory adaptation dynamically changes neural responses as a function of previous stimuli, profoundly impacting perception. The response changes induced by adaptation have been characterized in detail in individual neurons and at the population level after averaging across trials. However, it is not clear how adaptation modifies the aspects of the representations that relate more directly to the ability to perceive stimuli, such as their geometry and the noise structure in individual trials. To address this question, we recorded from a population of neurons in the mouse visual cortex and presented one stimulus (an oriented grating) more frequently than the others. We then analyzed these data in terms of representational geometry and studied the ability of a linear decoder to discriminate between similar visual stimuli based on the single-trial population responses. Surprisingly, the discriminability of stimuli near the adaptor increased, even though the responses of individual neurons to these stimuli decreased. This improvement arose mainly from how adaptation reshaped the geometry of the mean population responses, with changes in the noise structure playing a minor role. Locomotion modulated the population responses along a direction largely independent of adaptation, allowing the adaptation-induced increase in discriminability to persist during locomotion. Finally, artificial neural networks trained to reconstruct the visual stimulus under metabolic constraints reproduced the increase in discriminability and the decrease in responses. The paradoxical effects of adaptation are thus consistent with the efficient coding framework, allowing the brain to improve the representation of frequent stimuli while limiting the associated metabolic cost.