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dc.contributor.authorAdeel, Ahsan
dc.date.accessioned2020-05-04T06:38:00Z
dc.date.available2020-05-04T06:38:00Z
dc.date.issued2020-05-19
dc.identifier.citationAdeel, A. (2020) Conscious multisensory integration: Introducing a universal contextual field in biological and deep artificial neural networks, Frontiers in Computational Neuroscience, 14:15en
dc.identifier.issn1662-5188en
dc.identifier.doi10.3389/fncom.2020.00015
dc.identifier.urihttp://hdl.handle.net/2436/623196
dc.description© 2020 The Authors. Published by Frontiers Media. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.3389/fncom.2020.00015en
dc.description.abstractConscious awareness plays a major role in human cognition and adaptive behaviour, though its function in multisensory integration is not yet fully understood, hence, questions remain: How does the brain integrate the incoming multisensory signals with respect to different external environments? How are the roles of these multisensory signals defined to adhere to the anticipated behavioural-constraint of the environment? This work seeks to articulate a novel theory on conscious multisensory integration that addresses the aforementioned research challenges. Specifically, the well-established contextual field (CF) in pyramidal cells and coherent infomax theory [1][2] is split into two functionally distinctive integrated input fields: local contextual field (LCF) and universal contextual field (UCF). LCF defines the modulatory sensory signal coming from some other parts of the brain (in principle from anywhere in space-time) and UCF defines the outside environment and anticipated behaviour (based on past learning and reasoning). Both LCF and UCF are integrated with the receptive field (RF) to develop a new class of contextually-adaptive neuron (CAN), which adapts to changing environments. The proposed theory is evaluated using human contextual audio-visual (AV) speech modelling. Simulation results provide new insights into contextual modulation and selective multisensory information amplification/suppression. The central hypothesis reviewed here suggests that the pyramidal cell, in addition to the classical excitatory and inhibitory signals, receives LCF and UCF inputs. The UCF (as a steering force or tuner) plays a decisive role in precisely selecting whether to amplify/suppress the transmission of relevant/irrelevant feedforward signals, without changing the content e.g., which information is worth paying more attention to? This, as opposed to, unconditional excitatory and inhibitory activity in existing deep neural networks (DNNs), is called conditional amplification/suppression.en
dc.formatapplication/pdfen
dc.language.isoenen
dc.publisherFrontiers Media S.A.en
dc.relation.urlhttps://www.frontiersin.org/articles/10.3389/fncom.2020.00015/fullen
dc.subjectUniversal Contextual Fielden
dc.subjectPyramidal Cellen
dc.subjectMultisensory Integrationen
dc.subjectCoherent Infomax Neuronen
dc.subjectContextually-Adaptive Neuronen
dc.subjectdeep neural networken
dc.subject1103 Clinical Sciencesen
dc.subject1109 Neurosciencesen
dc.subjectUniversal Contextual Fielden
dc.subjectPyramidal Cellen
dc.subjectMultisensory Integrationen
dc.subjectCoherent Infomax Neuronen
dc.subjectContextually-Adaptive Neuronen
dc.subjectdeep neural networken
dc.titleConscious multisensory integration: Introducing a universal contextual field in biological and deep artificial neural networksen
dc.typeJournal articleen
dc.identifier.journalFrontiers in Computational Neuroscienceen
dc.date.updated2020-05-02T17:31:27Z
dc.identifier.articlenumber15
dc.identifier.articlenumber15
dc.date.accepted2020-02-07
rioxxterms.funderEngineering and Physical Sciences Research Councilen
rioxxterms.identifier.projectEP/M026981/1en
rioxxterms.versionVoRen
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/en
rioxxterms.licenseref.startdate2020-05-04en
dc.source.volume14
refterms.dateFCD2020-05-04T06:37:46Z
refterms.versionFCDVoR
refterms.dateFOA2020-05-04T06:38:01Z


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