The Convallis rule for unsupervised learning in cortical networks.

The phenomenology and cellular mechanisms of cortical synaptic plasticity are becoming known in increasing detail, but the computational principles by which cortical plasticity enables the development of sensory representations are unclear. Here we describe a framework for cortical synaptic plastici...

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Autores principales:	Pierre Yger, Kenneth D Harris
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2013
Materias:	Biology (General) QH301-705.5
Acceso en línea:	https://doaj.org/article/f0b035cca52d4f54a4d06465b108a713
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spelling	oai:doaj.org-article:f0b035cca52d4f54a4d06465b108a7132021-11-18T05:53:29ZThe Convallis rule for unsupervised learning in cortical networks.1553-734X1553-735810.1371/journal.pcbi.1003272https://doaj.org/article/f0b035cca52d4f54a4d06465b108a7132013-10-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24204224/?tool=EBIhttps://doaj.org/toc/1553-734Xhttps://doaj.org/toc/1553-7358The phenomenology and cellular mechanisms of cortical synaptic plasticity are becoming known in increasing detail, but the computational principles by which cortical plasticity enables the development of sensory representations are unclear. Here we describe a framework for cortical synaptic plasticity termed the "Convallis rule", mathematically derived from a principle of unsupervised learning via constrained optimization. Implementation of the rule caused a recurrent cortex-like network of simulated spiking neurons to develop rate representations of real-world speech stimuli, enabling classification by a downstream linear decoder. Applied to spike patterns used in in vitro plasticity experiments, the rule reproduced multiple results including and beyond STDP. However STDP alone produced poorer learning performance. The mathematical form of the rule is consistent with a dual coincidence detector mechanism that has been suggested by experiments in several synaptic classes of juvenile neocortex. Based on this confluence of normative, phenomenological, and mechanistic evidence, we suggest that the rule may approximate a fundamental computational principle of the neocortex.Pierre YgerKenneth D HarrisPublic Library of Science (PLoS)articleBiology (General)QH301-705.5ENPLoS Computational Biology, Vol 9, Iss 10, p e1003272 (2013)
institution	DOAJ
collection	DOAJ
language	EN
topic	Biology (General) QH301-705.5
spellingShingle	Biology (General) QH301-705.5 Pierre Yger Kenneth D Harris The Convallis rule for unsupervised learning in cortical networks.
description	The phenomenology and cellular mechanisms of cortical synaptic plasticity are becoming known in increasing detail, but the computational principles by which cortical plasticity enables the development of sensory representations are unclear. Here we describe a framework for cortical synaptic plasticity termed the "Convallis rule", mathematically derived from a principle of unsupervised learning via constrained optimization. Implementation of the rule caused a recurrent cortex-like network of simulated spiking neurons to develop rate representations of real-world speech stimuli, enabling classification by a downstream linear decoder. Applied to spike patterns used in in vitro plasticity experiments, the rule reproduced multiple results including and beyond STDP. However STDP alone produced poorer learning performance. The mathematical form of the rule is consistent with a dual coincidence detector mechanism that has been suggested by experiments in several synaptic classes of juvenile neocortex. Based on this confluence of normative, phenomenological, and mechanistic evidence, we suggest that the rule may approximate a fundamental computational principle of the neocortex.
format	article
author	Pierre Yger Kenneth D Harris
author_facet	Pierre Yger Kenneth D Harris
author_sort	Pierre Yger
title	The Convallis rule for unsupervised learning in cortical networks.
title_short	The Convallis rule for unsupervised learning in cortical networks.
title_full	The Convallis rule for unsupervised learning in cortical networks.
title_fullStr	The Convallis rule for unsupervised learning in cortical networks.
title_full_unstemmed	The Convallis rule for unsupervised learning in cortical networks.
title_sort	convallis rule for unsupervised learning in cortical networks.
publisher	Public Library of Science (PLoS)
publishDate	2013
url	https://doaj.org/article/f0b035cca52d4f54a4d06465b108a713
work_keys_str_mv	AT pierreyger theconvallisruleforunsupervisedlearningincorticalnetworks AT kennethdharris theconvallisruleforunsupervisedlearningincorticalnetworks AT pierreyger convallisruleforunsupervisedlearningincorticalnetworks AT kennethdharris convallisruleforunsupervisedlearningincorticalnetworks
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The Convallis rule for unsupervised learning in cortical networks.

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