Emergence of movement sensitive neurons' properties by learning a sparse code for natural moving images

Olshausen and Field demonstrated that a learning algorithm that attempts to generate a sparse code for natural scenes develops a complete family of localised, oriented, bandpass receptive fields, similar to those of 'simple cells' in V1. This paper describes an algorithm which finds a spar...

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Main Authors: Bogacz, R, Brown, M, Giraud-Carrier, C
Format: Conference item
Published: Neural information processing systems foundation 2001
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author Bogacz, R
Brown, M
Giraud-Carrier, C
author_facet Bogacz, R
Brown, M
Giraud-Carrier, C
author_sort Bogacz, R
collection OXFORD
description Olshausen and Field demonstrated that a learning algorithm that attempts to generate a sparse code for natural scenes develops a complete family of localised, oriented, bandpass receptive fields, similar to those of 'simple cells' in V1. This paper describes an algorithm which finds a sparse code for sequences of images that preserves information about the input. This algorithm when trained on natural video sequences develops bases representing the movement in particular directions with particular speeds, similar to the receptive fields of the movement-sensitive cells observed in cortical visual areas. Furthermore, in contrast to previous approaches to learning direction selectivity, the timing of neuronal activity encodes the phase of the movement, so the precise timing of spikes is crucially important to the information encoding.
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spelling oxford-uuid:1c5cf97a-cd68-43ed-b6af-2d013f514b6c2022-03-26T11:05:08ZEmergence of movement sensitive neurons' properties by learning a sparse code for natural moving imagesConference itemhttp://purl.org/coar/resource_type/c_5794uuid:1c5cf97a-cd68-43ed-b6af-2d013f514b6cSymplectic Elements at OxfordNeural information processing systems foundation2001Bogacz, RBrown, MGiraud-Carrier, COlshausen and Field demonstrated that a learning algorithm that attempts to generate a sparse code for natural scenes develops a complete family of localised, oriented, bandpass receptive fields, similar to those of 'simple cells' in V1. This paper describes an algorithm which finds a sparse code for sequences of images that preserves information about the input. This algorithm when trained on natural video sequences develops bases representing the movement in particular directions with particular speeds, similar to the receptive fields of the movement-sensitive cells observed in cortical visual areas. Furthermore, in contrast to previous approaches to learning direction selectivity, the timing of neuronal activity encodes the phase of the movement, so the precise timing of spikes is crucially important to the information encoding.
spellingShingle Bogacz, R
Brown, M
Giraud-Carrier, C
Emergence of movement sensitive neurons' properties by learning a sparse code for natural moving images
title Emergence of movement sensitive neurons' properties by learning a sparse code for natural moving images
title_full Emergence of movement sensitive neurons' properties by learning a sparse code for natural moving images
title_fullStr Emergence of movement sensitive neurons' properties by learning a sparse code for natural moving images
title_full_unstemmed Emergence of movement sensitive neurons' properties by learning a sparse code for natural moving images
title_short Emergence of movement sensitive neurons' properties by learning a sparse code for natural moving images
title_sort emergence of movement sensitive neurons properties by learning a sparse code for natural moving images
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AT brownm emergenceofmovementsensitiveneuronspropertiesbylearningasparsecodefornaturalmovingimages
AT giraudcarrierc emergenceofmovementsensitiveneuronspropertiesbylearningasparsecodefornaturalmovingimages