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dc.contributor.authorChristodoulou, Chris C.en
dc.contributor.authorClarkson, Trevor G.en
dc.contributor.authorBugmann, Guidoen
dc.contributor.authorTaylor, John G.en
dc.creatorChristodoulou, Chris C.en
dc.creatorClarkson, Trevor G.en
dc.creatorBugmann, Guidoen
dc.creatorTaylor, John G.en
dc.description.abstractThis paper examines the computational role of inhibition as it moves towards balancing concurrent excitation using the biologically-inspired Temporal Noisy-Leaky Integrator (TNLI) neuron model. The TNLI incorporates hyperpolarizing inhibition with negative current pulses of controlled shapes and it also separates dendritic from somatic integration. The function of inhibition is investigated by examining its effect on the transfer function of the neuron and on the membrane potential. Increasing inhibition leads to greater membrane potential fluctuations as well as greater amplitude variations for a given level of mean input current. This added variance leads to decreasing the slope of the neuron's transfer function (mean input current vs mean output frequency), effectively reducing the gain of the input/output sigmoiden
dc.description.abstractinhibition can therefore be used as a means of controlling the gain of the transfer function. Moreover, we demonstrate that in the case of balanced excitation and inhibition (where the neuron is totally driven by membrane potential fluctuations), the neuron's firing rate can be controlled by the level of mean input frequency.en
dc.sourceProceedings of the International Joint Conference on Neural Networksen
dc.sourceInternational Joint Conference on Neural Networks (IJCNN'2000)en
dc.subjectMathematical modelsen
dc.subjectNeural networksen
dc.subjectTransfer functionsen
dc.subjectTemporal noisy-leaky integrator (TNLI)en
dc.titleAnalysis of fluctuation-induced firing in the presence of inhibitionen
dc.description.endingpage120 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeConference Objecten
dc.description.notes<p>Sponsors: IEEE Neural Network Councilen
dc.description.notesInternational Neural Network Societyen
dc.description.notesEuropean Neural Network Societyen
dc.description.notesConference code: 57394en
dc.description.notesCited By :3</p>en
dc.contributor.orcidChristodoulou, Chris C. [0000-0001-9398-5256]

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