Studying the role of synchronized and chaotic spiking neural ensembles in neural information processing

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dc.contributor.author Rossello, J.L.
dc.contributor.author Canals, V.
dc.contributor.author Oliver, A.
dc.contributor.author Morro, A.
dc.date.accessioned 2020-04-28T06:38:39Z
dc.identifier.uri http://hdl.handle.net/11201/152158
dc.description.abstract [eng] The brain is characterized by performing many diverse processing tasks ranging from elaborate processes such as pattern recognition, memory or decision making to more simple functionalities such as linear filtering in image processing. Understanding the mechanisms by which the brain is able to produce such a different range of cortical operations remains a fundamental problem in neuroscience. Here we show a study about which processes are related to chaotic and synchronized states based on the study of in-silico implementation of Stochastic Spiking Neural Networks (SSNN). The measurements obtained reveal that chaotic neural ensembles are excellent transmission and convolution systems since mutual information between signals is minimized. At the same time, synchronized cells (that can be understood as ordered states of the brain) can be associated to more complex nonlinear computations. In this sense, we experimentally show that complex and quick pattern recognition processes arise when both synchronized and chaotic states are mixed. These measurements are in accordance with in vivo observations related to the role of neural synchrony in pattern recognition and to the speed of the real biological process. We also suggest that the high-level adaptive mechanisms of the brain that are the Hebbian and non-Hebbian learning rules can be understood as processes devoted to generate the appropriate clustering of both synchronized and chaotic ensembles. The measurements obtained from the hardware implementation of different types of neural systems suggest that the brain processing can be governed by the superposition of these two complementary states with complementary functionalities (nonlinear processing for synchronized states and information convolution and parallelization for chaotic).
dc.format application/pdf
dc.relation.isformatof Versió postprint del document publicat a: https://doi.org/10.1142/S0129065714300034
dc.relation.ispartof International Journal of Neural Systems, 2014, vol. 24, num. 5, p. 1-11
dc.rights (c) World Scientific Publishing Company, 2014
dc.subject.classification 53 - Física
dc.subject.other 53 - Physics
dc.title Studying the role of synchronized and chaotic spiking neural ensembles in neural information processing
dc.type info:eu-repo/semantics/article
dc.type info:eu-repo/semantics/acceptedVersion
dc.date.updated 2020-04-28T06:38:39Z
dc.date.embargoEndDate info:eu-repo/date/embargoEnd/2026-12-31
dc.embargo 2026-12-31
dc.rights.accessRights info:eu-repo/semantics/embargoedAccess
dc.identifier.doi https://doi.org/10.1142/S0129065714300034


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