Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex

doi:10.1016/j.neuron.2015.05.037

Paper

Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex

Published Jun 17, 2015 · Jasper Poort, Adil G. Khan, Marius Pachitariu

Neuron

Q1 SJR score

367

Citations

20

Influential Citations

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Abstract

Abstract removed due to Elsevier request; this does not indicate any issues with the research. Click the full text link above to read the abstract and view the original source.

Non-RCT

Animal Study

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Key takeawayLearning enhances sensory and non-sensory representations in primary visual cortex, improving performance in visual tasks by stabilizing existing and recruiting new neurons selective for task-relevant stimuli.

PopulationOlder adults (50-71 years)

Sample size24

MethodsObservational

OutcomesBody Mass Index projections

ResultsSocial networks mitigate obesity in older groups.

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Paper

Learning Enhances Sensory and Multiple Non-sensory Representations in Primary Visual Cortex

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References

Citations

Movie reconstruction from mouse visual cortex activity

High-quality reconstructions of 10-second movies from mouse visual cortex activity can be achieved using two-photon calcium imaging data, with a 2-fold increase in pixel-by-pixel correlation compared to static images.

Hierarchy of prediction errors shapes the learning of context-dependent sensory representations

Context shapes sensory processing in the brain, with layer 2/3 somatosensory neurons enhancing reward response and lateral orbitofrontal cortex signaling loss of confidence in outcome prediction.

Discretized representations in V1 predict suboptimal orientation discrimination

Discretized representations in V1 neurons in sensory cortices predict suboptimal orientation discrimination in mice, as they relocate activity to task-relevant domains.

Coordinated multi-level adaptations across neocortical areas during task learning

During task learning, multi-level adaptations within and across cortical areas contribute to refinement of sensory processing and decision-making.

A mathematical theory of relational generalization in transitive inference.

Artificial learning systems without transitivity built in can perform relational generalizations and exhibit behaviors seen in humans and animals, demonstrating their similarity to the brain.