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dc.contributor.authorBugmann, G.en
dc.contributor.authorChristodoulou, Chris C.en
dc.contributor.authorTaylor, J. G.en
dc.creatorBugmann, G.en
dc.creatorChristodoulou, Chris C.en
dc.creatorTaylor, J. G.en
dc.date.accessioned2019-11-13T10:38:49Z
dc.date.available2019-11-13T10:38:49Z
dc.date.issued1997
dc.identifier.urihttp://gnosis.library.ucy.ac.cy/handle/7/53655
dc.description.abstractPartial reset is a simple and powerful tool for controlling the irregularity of spike trains fired by a leaky integrator neuron model with random inputs. In particular, a single neuron model with a realistic membrane time constant of 10 ms can reproduce the highly irregular firing of cortical neurons reported by Softky and Koch (1993). In this article, the mechanisms by which partial reset affects the firing pattern are investigated. It is shown theoretically that partial reset is equivalent to the use of a time-dependent threshold, similar to a technique proposed by Wilbur and Rinzel (1983) to produce high irregularity. This equivalent model allows establishing that temporal integration and fluctuation detection can coexist and cooperate to cause highly irregular firing. This study also reveals that reverse correlation curves cannot be used reliably to assess the causes of firing. For instance, they do not reveal temporal integration when it takes place. Further, the peak near time zero does not always indicate coincidence detection. An alternative qualitative method is proposed here for that later purpose. Finally, it is noted that as the reset becomes weaker, the firing pattern shows a progressive transition from regular firing, to random, to temporally clustered, and eventually to bursting firing. Concurrently the slope of the transfer function increases. Thus, simulations suggest a correlation between high gain and highly irregular firing.en
dc.sourceNeural computationen
dc.source.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-0001559694&partnerID=40&md5=46c5b2e65a5b8c0c674bda11bf47329b
dc.titleRole of Temporal Integration and Fluctuation Detection in the Highly Irregular Firing of a Leaky Integrator Neuron Model with Partial Reseten
dc.typeinfo:eu-repo/semantics/article
dc.description.volume9
dc.description.issue5
dc.description.startingpage985
dc.description.endingpage1000
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 :59</p>en
dc.source.abbreviationNeural Comp.en
dc.contributor.orcidChristodoulou, Chris C. [0000-0001-9398-5256]


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