Latching dynamics in neural networks with synaptic depression

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dc.contributor.author Aguilar, Carlos
dc.contributor.author Chossat, Pascal
dc.contributor.author Krupa, Martin
dc.contributor.author Lavigne, Frederic
dc.date.accessioned 2017-09-26T11:39:23Z
dc.date.available 2017-09-26T11:39:23Z
dc.date.issued 2017
dc.identifier.citation Aguilar, C., Chossat, P., Krupa, M. and Lavigne, F. (2017) 'Latching dynamics in neural networks with synaptic depression', PLOS ONE, 12(8), e0183710 (29pp). doi: 10.1371/journal.pone.0183710 en
dc.identifier.volume 12
dc.identifier.issued 8
dc.identifier.issn 1932-6203
dc.identifier.uri http://hdl.handle.net/10468/4810
dc.identifier.doi 10.1371/journal.pone.0183710
dc.description.abstract Prediction is the ability of the brain to quickly activate a target concept in response to a related stimulus (prime). Experiments point to the existence of an overlap between the populations of the neurons coding for different stimuli, and other experiments show that prime-target relations arise in the process of long term memory formation. The classical modelling paradigm is that long term memories correspond to stable steady states of a Hopfield network with Hebbian connectivity. Experiments show that short term synaptic depression plays an important role in the processing of memories. This leads naturally to a computational model of priming, called latching dynamics; a stable state (prime) can become unstable and the system may converge to another transiently stable steady state (target). Hopfield network models of latching dynamics have been studied by means of numerical simulation, however the conditions for the existence of this dynamics have not been elucidated. In this work we use a combination of analytic and numerical approaches to confirm that latching dynamics can exist in the context of a symmetric Hebbian learning rule, however lacks robustness and imposes a number of biologically unrealistic restrictions on the model. In particular our work shows that the symmetry of the Hebbian rule is not an obstruction to the existence of latching dynamics, however fine tuning of the parameters of the model is needed. en
dc.format.mimetype application/pdf en
dc.language.iso en en
dc.publisher Public Library of Science en
dc.relation.uri http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0183710
dc.rights © 2017, Aguilar 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. en
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Inferior temporal cortex en
dc.subject Long term memory en
dc.subject Monkey inferotemporal cortex en
dc.subject Semantic priming shift en
dc.subject Prefrontal cortex en
dc.subject Associative memory en
dc.subject Pyramidal neurons en
dc.subject Cortical network en
dc.subject Working memory en
dc.subject Abstract rules en
dc.title Latching dynamics in neural networks with synaptic depression en
dc.type Article (peer-reviewed) en
dc.internal.authorcontactother Martin Krupa, Applied Mathematics, University College Cork, Cork, Ireland. E-mail: m.krupa@ucc.ie en
dc.internal.availability Full text available en
dc.description.version Published Version en
dc.internal.wokid WOS:000408438600047
dc.contributor.funder European Research Council
dc.description.status Peer reviewed en
dc.identifier.journaltitle PLoS ONE en
dc.internal.IRISemailaddress m.krupa@ucc.ie en
dc.identifier.articleid e0183710
dc.relation.project info:eu-repo/grantAgreement/EC/FP7::SP2::ERC/227747/EU/From single neurons to visual perception/NERVI


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© 2017, Aguilar 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. Except where otherwise noted, this item's license is described as © 2017, Aguilar 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.
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