Distinct response selectivity changes in primary visual and parietal cortex during visual discrimination learning

Read the full article See related articles

Discuss this preprint

Start a discussion What are Sciety discussions?

Listed in

This article is not in any list yet, why not save it to one of your lists.
Log in to save this article

Abstract

When animals learn the behavioral relevance of sensory features, response selectivity in primary sensory areas increases for those features. However, the effect of learning on neuronal activity in higher-level areas associated with decision-making in the parietal cortex, compared to primary sensory cortex, is not yet known. We used two-photon calcium imaging to determine how learning modifies neural representations in primary visual cortex (V1) and posterior parietal cortex (PPC) in a visual go/no-go orientation discrimination task. We found that behavior improvements after learning were associated with increased neuronal selectivity in both V1 and PPC. The increased selectivity in PPC was mainly driven by neurons preferring the rewarded go stimulus, while, in V1, neurons increased their selectivity for both the rewarded go and unrewarded no-go stimulus. Furthermore, feature preference was robust in V1 neurons but reorganized in PPC after learning. Finally, feature preference was preserved across contexts in V1 but not in PPC after learning, where many neurons switched their preference to the rewarded feature during active task engagement. Our results demonstrate that learning a visually-guided discrimination task increased information about relevant sensory features through distinct changes in the bottom and top levels of the visual cortical hierarchy. Visual cortex neurons encode visual features with increased reliability but with preserved feature preferences, while parietal neurons reorganize their feature preferences in a task-specific manner.

Highlights

  • V1 and PPC response selectivity increases after visual discrimination learning.

  • Selectivity increase in PPC for rewarded features, but in V1 for both rewarded and unrewarded features.

  • Feature preference robust in V1 but altered in PPC across learning.

  • Feature preference after learning preserved across contexts in V1 but not PPC.

Article activity feed