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dc.contributor.authorChristodoulou, Chris C.en
dc.contributor.authorBanfield, G.en
dc.contributor.authorCleanthous, A.en
dc.creatorChristodoulou, Chris C.en
dc.creatorBanfield, G.en
dc.creatorCleanthous, A.en
dc.date.accessioned2019-11-13T10:39:15Z
dc.date.available2019-11-13T10:39:15Z
dc.date.issued2010
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/53704
dc.description.abstractSelf-control can be defined as choosing a large delayed reward over a small immediate reward, while precommitment is the making of a choice with the specific aim of denying oneself future choices. Humans recognise that they have self-control problems and attempt to overcome them by applying precommitment. Problems in exercising self-control, suggest a conflict between cognition and motivation, which has been linked to competition between higher and lower brain functions (representing the frontal lobes and the limbic system respectively). This premise of an internal process conflict, lead to a behavioural model being proposed, based on which, we implemented a computational model for studying and explaining self-control through precommitment behaviour. Our model consists of two neural networks, initially non-spiking and then spiking ones, representing the higher and lower brain systems viewed as cooperating for the benefit of the organism. The non-spiking neural networks are of simple feed forward multilayer type with reinforcement learning, one with selective bootstrap weight update rule, which is seen as myopic, representing the lower brain and the other with the temporal difference weight update rule, which is seen as far-sighted, representing the higher brain. The spiking neural networks are implemented with leaky integrate-and-fire neurons with learning based on stochastic synaptic transmission. The differentiating element between the two brain centres in this implementation is based on the memory of past actions determined by an eligibility trace time constant. As the structure of the self-control problem can be likened to the Iterated Prisoner's Dilemma (IPD) game in that cooperation is to defection what self-control is to impulsiveness or what compromising is to insisting, we implemented the neural networks as two players, learning simultaneously but independently, competing in the IPD game. With a technique resembling the precommitment effect, whereby the payoffs for the dilemma cases in the IPD payoff matrix are differentially biased (increased or decreased), it is shown that increasing the precommitment effect (through increasing the differential bias) increases the probability of cooperating with oneself in the future, irrespective of whether the implementation is with spiking or non-spiking neural networks. © 2009 Elsevier Ltd.en
dc.sourceJournal of Physiology Parisen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-77952545546&doi=10.1016%2fj.jphysparis.2009.11.013&partnerID=40&md5=be624f55e1c64d37739c4d2b1cd5cf95
dc.subjectLearningen
dc.subjecttheoryen
dc.subjectarticleen
dc.subjectmathematical modelen
dc.subjectdecision makingen
dc.subjectHumansen
dc.subjectcontrolled studyen
dc.subjectAnimalsen
dc.subjectNeuronsen
dc.subjectNeural Networks (Computer)en
dc.subjectmathematical analysisen
dc.subjectnerve cell networken
dc.subjectStochastic Processesen
dc.subjectReinforcement learningen
dc.subjectspike waveen
dc.subjectsynaptic transmissionen
dc.subjectAction Potentialsen
dc.subjectbootstrappingen
dc.subjectbrain functionen
dc.subjectChoice Behavioren
dc.subjectexperimental testen
dc.subjectgameen
dc.subjectGame Theoryen
dc.subjectimpulsivenessen
dc.subjectIterated Prisoner's Dilemmaen
dc.subjectIterated Prsisoner Dilemma gameen
dc.subjectModels, Neurologicalen
dc.subjectmotivationen
dc.subjectnerve stimulationen
dc.subjectneuropsychologyen
dc.subjectPrecommitmenten
dc.subjectreinforcementen
dc.subjectReinforcement (Psychology)en
dc.subjectself controlen
dc.subjectSelf-controlen
dc.subjectSpiking neural networksen
dc.subjectstimulus responseen
dc.titleSelf-control with spiking and non-spiking neural networks playing gamesen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.1016/j.jphysparis.2009.11.013
dc.description.volume104
dc.description.issue3-4
dc.description.startingpage108
dc.description.endingpage117
dc.author.faculty002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences
dc.author.departmentΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeArticleen
dc.description.notes<p>Cited By :6</p>en
dc.source.abbreviationJ.Physiol.Parisen
dc.contributor.orcidChristodoulou, Chris C. [0000-0001-9398-5256]
dc.gnosis.orcid0000-0001-9398-5256


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