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Learning invariant object and spatial view representations in the brain using slow unsupervised learning

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Rolls, Edmund T. (2021) Learning invariant object and spatial view representations in the brain using slow unsupervised learning. Frontiers in Computational Neuroscience, 15 . 686239. doi:10.3389/fncom.2021.686239

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Official URL: https://doi.org/10.3389/fncom.2021.686239

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Abstract

First, neurophysiological evidence for the learning of invariant representations in the inferior temporal visual cortex is described. This includes object and face representations with invariance for position, size, lighting, view and morphological transforms in the temporal lobe visual cortex; global object motion in the cortex in the superior temporal sulcus; and spatial view representations in the hippocampus that are invariant with respect to eye position, head direction, and place. Second, computational mechanisms that enable the brain to learn these invariant representations are proposed. For the ventral visual system, one key adaptation is the use of information available in the statistics of the environment in slow unsupervised learning to learn transform-invariant representations of objects. This contrasts with deep supervised learning in artificial neural networks, which uses training with thousands of exemplars forced into different categories by neuronal teachers. Similar slow learning principles apply to the learning of global object motion in the dorsal visual system leading to the cortex in the superior temporal sulcus. The learning rule that has been explored in VisNet is an associative rule with a short-term memory trace. The feed-forward architecture has four stages, with convergence from stage to stage. This type of slow learning is implemented in the brain in hierarchically organized competitive neuronal networks with convergence from stage to stage, with only 4-5 stages in the hierarchy. Slow learning is also shown to help the learning of coordinate transforms using gain modulation in the dorsal visual system extending into the parietal cortex and retrosplenial cortex. Representations are learned that are in allocentric spatial view coordinates of locations in the world and that are independent of eye position, head direction, and the place where the individual is located. This enables hippocampal spatial view cells to use idiothetic, self-motion, signals for navigation when the view details are obscured for short periods.

Item Type: Journal Article
Subjects: B Philosophy. Psychology. Religion > BF Psychology
Q Science > QP Physiology
Divisions: Faculty of Science > Computer Science
SWORD Depositor: Library Publications Router
Library of Congress Subject Headings (LCSH): Visual cortex, Neural networks (Neurobiology), Visual perception -- Research, Neural circuitry
Journal or Publication Title: Frontiers in Computational Neuroscience
Publisher: Frontiers Media S.A.
ISSN: 1662-5188
Official Date: 21 July 2021
Dates:
DateEvent
21 July 2021Published
29 June 2021Accepted
Volume: 15
Article Number: 686239
DOI: 10.3389/fncom.2021.686239
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIED[MRC] Medical Research Councilhttp://dx.doi.org/10.13039/501100000265
UNSPECIFIEDWellcome Trusthttp://dx.doi.org/10.13039/100010269
UNSPECIFIEDHuman Frontier Science Programhttp://dx.doi.org/10.13039/501100000854
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