Choice-correlated activity fluctuations underlie learning of neuronal category representation

Tatiana A. Engel, Warasinee Chaisangmongkon, David J. Freedman and Xiao-Jing Wang ()
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Tatiana A. Engel: Yale University School of Medicine, Kavli Institute for Neuroscience
Warasinee Chaisangmongkon: Yale University School of Medicine, Kavli Institute for Neuroscience
David J. Freedman: The University of Chicago
Xiao-Jing Wang: Yale University School of Medicine, Kavli Institute for Neuroscience

Nature Communications, 2015, vol. 6, issue 1, 1-12

Abstract: 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 and categorical tuning, and a positive correlation between category selectivity and choice probability. Beyond demonstrating a circuit mechanism for categorization, the present work suggests a key role of plastic top-down feedback in simultaneously shaping both neural tuning and correlated neural variability.

Date: 2015
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DOI: 10.1038/ncomms7454

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