Schäfer, R., Vasilaki, E. orcid.org/0000-0003-3705-7070 and Senn, W. (2009) Adaptive gain modulation in V1 explains contextual modifications during bisection learning. PLoS Computational Biology, 5 (12). e1000617. ISSN 1553-734X
Abstract
The neuronal processing of visual stimuli in primary visual cortex (V1) can be modified by perceptual training. Training in bisection discrimination, for instance, changes the contextual interactions in V1 elicited by parallel lines. Before training, two parallel lines inhibit their individual V1-responses. After bisection training, inhibition turns into non-symmetric excitation while performing the bisection task. Yet, the receptive field of the V1 neurons evaluated by a single line does not change during task performance. We present a model of recurrent processing in V1 where the neuronal gain can be modulated by a global attentional signal. Perceptual learning mainly consists in strengthening this attentional signal, leading to a more effective gain modulation. The model reproduces both the psychophysical results on bisection learning and the modified contextual interactions observed in V1 during task performance. It makes several predictions, for instance that imagery training should improve the performance, or that a slight stimulus wiggling can strongly affect the representation in V1 while performing the task. We conclude that strengthening a top-down induced gain increase can explain perceptual learning, and that this top-down signal can modify lateral interactions within V1, without significantly changing the classical receptive field of V1 neurons.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2009 Schäfer et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
Keywords: | Algorithms; Computational Biology; Learning; Models, Neurological; Normal Distribution; Visual Cortex |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 10 May 2016 10:28 |
Last Modified: | 10 May 2016 10:34 |
Published Version: | http://dx.doi.org/10.1371/journal.pcbi.1000617 |
Status: | Published |
Publisher: | Public Library of Science |
Refereed: | Yes |
Identification Number: | 10.1371/journal.pcbi.1000617 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:99460 |