Choice-correlated activity fluctuations underlie learning of neuronal category representation.

ABSTRACT: The ability to categorize stimuli into discrete behaviourally relevant groups is an essential cognitive function. To elucidate the neural mechanisms underlying categorization, we constructed a cortical circuit model that is capable of learning a motion categorization task through reward-dependent plasticity. Here we show that stable category representations develop in neurons intermediate to sensory and decision layers if they exhibit choice-correlated activity fluctuations (choice probability). In the model, choice probability and task-specific interneuronal correlations emerge from plasticity of top-down projections from decision neurons. Specific model predictions are confirmed by analysis of single-neuron activity from the monkey parietal cortex, which reveals a mixture of directional

SUBMITTER: Engel TA 

PROVIDER: S-EPMC4382677 | biostudies-literature | 2015 Mar

REPOSITORIES: biostudies-literature