Latching dynamics in neural networks with synaptic depression

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dc.contributor.authorAguilar, Carlos
dc.contributor.authorChossat, Pascal
dc.contributor.authorKrupa, Martin
dc.contributor.authorLavigne, Frederic
dc.contributor.funderEuropean Research Council
dc.date.accessioned2017-09-26T11:39:23Z
dc.date.available2017-09-26T11:39:23Z
dc.date.issued2017
dc.description.abstractPrediction 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.description.statusPeer revieweden
dc.description.versionPublished Versionen
dc.format.mimetypeapplication/pdfen
dc.identifier.articleide0183710
dc.identifier.citationAguilar, 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.0183710en
dc.identifier.doi10.1371/journal.pone.0183710
dc.identifier.issn1932-6203
dc.identifier.issued8
dc.identifier.journaltitlePLoS ONEen
dc.identifier.urihttps://hdl.handle.net/10468/4810
dc.identifier.volume12
dc.language.isoenen
dc.publisherPublic Library of Scienceen
dc.relation.projectinfo:eu-repo/grantAgreement/EC/FP7::SP2::ERC/227747/EU/From single neurons to visual perception/NERVI
dc.relation.urihttp://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.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectInferior temporal cortexen
dc.subjectLong term memoryen
dc.subjectMonkey inferotemporal cortexen
dc.subjectSemantic priming shiften
dc.subjectPrefrontal cortexen
dc.subjectAssociative memoryen
dc.subjectPyramidal neuronsen
dc.subjectCortical networken
dc.subjectWorking memoryen
dc.subjectAbstract rulesen
dc.titleLatching dynamics in neural networks with synaptic depressionen
dc.typeArticle (peer-reviewed)en
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