Population coupling predicts the plasticity of stimulus responses in cortical circuits

Abstract

Long-term imaging of sensory cortex reveals a diverse range of stimulus response stability: some neurons retain stimulus responses that are stable over days whereas other neurons have highly plastic stimulus responses. Using a recurrent network model, we explore whether this observation could be due to an underlying diversity in the synaptic plasticity of neurons. We find that, in a network with diverse learning rates, neurons with fast rates are more coupled to population activity than neurons with slow rates. This phenomenon, which we call a plasticity-coupling link, surprisingly predicts that neurons with high population coupling exhibit more long-term stimulus response variability than neurons with low population coupling. We substantiate this prediction using recordings from the Allen Brain Observatory which track the orientation preferences of 15,000 neurons in mouse visual cortex. In agreement with our model, a neurons population coupling is correlated with the plasticity of its orientation preference. Finally, we show that high population coupling helps plastic neurons alter their stimulus preference during a simple perceptual learning task, but hinders the ability of stable neurons to provide an instructive signal for learning. This suggests a particular functional architecture: a stable backbone of stimulus representation formed by neurons with slow synaptic plasticity and low population coupling, on top of which lies a flexible substrate of neurons with fast synaptic plasticity and high population coupling.