Learning shapes cortical dynamics to enhance integration of relevant sensory input
Adaptive sensory behavior is thought to depend on processing in recurrent cortical circuits, but how dynamics in these circuits shapes the integration and transmission of sensory information is not well understood. Here, we study neural coding in recurrently connected networks of neurons driven by sensory input. We show analytically how information available in the network output varies with the alignment between feedforward input and the integrating modes of the circuit dynamics. In light of this theory, we analyzed neural population activity in the visual cortex of mice that learned to discriminate visual features. We found that over learning, slow patterns of network dynamics realigned to better integrate input relevant to the discrimination task. This realignment of network dynamics could be explained by changes in excitatory-inhibitory connectivity amongst neurons tuned to relevant features. These results suggest that learning tunes the temporal dynamics of cortical circuits to optimally integrate relevant sensory input. HighlightsO_LIA new theoretical principle links recurrent circuit dynamics to optimal sensory coding C_LIO_LIPredicts that high-SNR input dimensions activate slowly decaying modes of dynamics C_LIO_LIPopulation dynamics in primary visual cortex realign during learning as predicted C_LIO_LIStimulus-specific changes in E-I connectivity in recurrent circuits explain realignment C_LI