4.8 Article

Learning enhances encoding of time and temporal surprise in mouse primary sensory cortex

Journal

NATURE COMMUNICATIONS
Volume 13, Issue 1, Pages -

Publisher

NATURE PORTFOLIO
DOI: 10.1038/s41467-022-33141-y

Keywords

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Funding

  1. NINDS/NIH [R01NS094659, R01NS069679, F31NS105490]
  2. NSF Graduate Student Research Fellowship
  3. Kavli Institute for Brain Science

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This study demonstrates that learning significantly alters the representation of neurons in the superficial layers of the mouse cortex, expanding the population of neurons sensitive to touch and behavioral choice. Training also improves encoding of the passage of time.
Activity in the superficial layers of the sensory cortex is believed to be largely driven by incoming sensory stimuli. Here the authors demonstrate how learning changes neural responses to sensations according to both behavioral relevance and timing, suggesting a high degree of non-sensory modulation. Primary sensory cortex has long been believed to play a straightforward role in the initial processing of sensory information. Yet, the superficial layers of cortex overall are sparsely active, even during sensory stimulation; additionally, cortical activity is influenced by other modalities, task context, reward, and behavioral state. Our study demonstrates that reinforcement learning dramatically alters representations among longitudinally imaged neurons in superficial layers of mouse primary somatosensory cortex. Learning an object detection task recruits previously unresponsive neurons, enlarging the neuronal population sensitive to touch and behavioral choice. Cortical responses decrease upon repeated stimulus presentation outside of the behavioral task. Moreover, training improves population encoding of the passage of time, and unexpected deviations in trial timing elicit even stronger responses than touches do. In conclusion, the superficial layers of sensory cortex exhibit a high degree of learning-dependent plasticity and are strongly modulated by non-sensory but behaviorally-relevant features, such as timing and surprise.

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