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Learning structure of sensory inputs with synaptic plasticity leads to interference

Chrol-Cannon, J and Jin, Y (2015) Learning structure of sensory inputs with synaptic plasticity leads to interference Frontiers in Computational Neuroscience, 9, 103.

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Abstract

Synaptic plasticity is often explored as a form of unsupervised adaptation in cortical microcircuits to learn the structure of complex sensory inputs and thereby improve performance of classification and prediction. The question of whether the specific structure of the input patterns is encoded in the structure of neural networks has been largely neglected. Existing studies that have analyzed input-specific structural adaptation have used simplified, synthetic inputs in contrast to complex and noisy patterns found in real-world sensory data. In this work, input-specific structural changes are analyzed for three empirically derived models of plasticity applied to three temporal sensory classification tasks that include complex, real-world visual and auditory data. Two forms of spike-timing dependent plasticity (STDP) and the Bienenstock-Cooper-Munro (BCM) plasticity rule are used to adapt the recurrent network structure during the training process before performance is tested on the pattern recognition tasks. It is shown that synaptic adaptation is highly sensitive to specific classes of input pattern. However, plasticity does not improve the performance on sensory pattern recognition tasks, partly due to synaptic interference between consecutively presented input samples. The changes in synaptic strength produced by one stimulus are reversed by the presentation of another, thus largely preventing input-specific synaptic changes from being retained in the structure of the network. To solve the problem of interference, we suggest that models of plasticity be extended to restrict neural activity and synaptic modification to a subset of the neural circuit, which is increasingly found to be the case in experimental neuroscience.

Item Type: Article
Divisions : Faculty of Engineering and Physical Sciences > Computing Science
Authors :
AuthorsEmailORCID
Chrol-Cannon, JUNSPECIFIEDUNSPECIFIED
Jin, YUNSPECIFIEDUNSPECIFIED
Date : 5 August 2015
Identification Number : 10.3389/fncom.2015.00103
Copyright Disclaimer : This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission. © 2015 Chrol-Cannon and Jin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Uncontrolled Keywords : Science & Technology, Life Sciences & Biomedicine, Mathematical & Computational Biology, Neurosciences, Neurosciences & Neurology, synaptic plasticity, spiking neural networks, recurrent neural networks, inference, pattern recognition, TEXTURE-DISCRIMINATION, SELF-ORGANIZATION, SPIKING NEURONS, NEURAL-NETWORKS, VISUAL-CORTEX, MODEL
Related URLs :
Additional Information : This Document is Protected by copyright and was first published by Frontiers. All rights reserved. it is reproduced with permission. © 2015 Chrol-Cannon and Jin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Depositing User : Symplectic Elements
Date Deposited : 02 Mar 2016 11:11
Last Modified : 02 Mar 2016 11:11
URI: http://epubs.surrey.ac.uk/id/eprint/809924

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