Choice-correlated activity fluctuations underlie learning of neuronal category representation

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. The ability to categorize stimuli into discrete behaviourally relevant groups is an essential cognitive function. Here, the authors demonstrate a critical role for choice-correlated activity fluctuations in the emergence of stable cortical category representations.

• Publication year

  2015

• Venue

  Nature Communications

• Publication date

  2015-03-11

• Fields of study

  Biology, Medicine, Psychology

• Identifiers
  DOI 10.1038/ncomms7454PMID 25759251PMCID 4382677
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar, PubMed

• No claims are published for this paper.

• No concepts are published for this paper.

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