Perceptual Learning via Modification of Cortical Top-Down Signals

Schäfer, Roland; Vasilaki, Eleni; Senn, Walter (2007). Perceptual Learning via Modification of Cortical Top-Down Signals. PLoS computational biology, 3(8), e165. San Francisco, Calif.: Public Library of Science 10.1371/journal.pcbi.0030165

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The primary visual cortex (V1) is pre-wired to facilitate the extraction of behaviorally important visual features. Collinear edge detectors in V1, for instance, mutually enhance each other to improve the perception of lines against a noisy background. The same pre-wiring that facilitates line extraction, however, is detrimental when subjects have to discriminate the brightness of different line segments. How is it possible to improve in one task by unsupervised practicing, without getting worse in the other task? The classical view of perceptual learning is that practicing modulates the feedforward input stream through synaptic modifications onto or within V1. However, any rewiring of V1 would deteriorate other perceptual abilities different from the trained one. We propose a general neuronal model showing that perceptual learning can modulate top-down input to V1 in a task-specific way while feedforward and lateral pathways remain intact. Consistent with biological data, the model explains how context-dependent brightness discrimination is improved by a top-down recruitment of recurrent inhibition and a top-down induced increase of the neuronal gain within V1. Both the top-down modulation of inhibition and of neuronal gain are suggested to be universal features of cortical microcircuits which enable perceptual learning.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Pre-clinic Human Medicine > Institute of Physiology

UniBE Contributor:

Senn, Walter

Subjects:

600 Technology > 610 Medicine & health

ISSN:

1553-734X

ISBN:

17715996

Publisher:

Public Library of Science

Language:

English

Submitter:

Factscience Import

Date Deposited:

04 Oct 2013 14:53

Last Modified:

05 Dec 2022 14:16

Publisher DOI:

10.1371/journal.pcbi.0030165

PubMed ID:

17715996

Web of Science ID:

000249767100014

BORIS DOI:

10.7892/boris.22299

URI:

https://boris.unibe.ch/id/eprint/22299 (FactScience: 33921)

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