dc.contributor.author | Christodoulou, Chris C. | en |
dc.contributor.author | Clarkson, T. | en |
dc.contributor.editor | Mira J. | en |
dc.contributor.editor | Sandoval F. | en |
dc.creator | Christodoulou, Chris C. | en |
dc.creator | Clarkson, T. | en |
dc.date.accessioned | 2019-11-13T10:39:15Z | |
dc.date.available | 2019-11-13T10:39:15Z | |
dc.date.issued | 1995 | |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/53709 | |
dc.description.abstract | The types of spike trains recorded in real neurons from different parts of the brain, can either be completely random or bursty. Certainly, at very, high firing rates regular spike trains are observed. This paper examines the neurobiological spike trains observed experimentally and analytically and presents how they can be modelled and accounted for by using the biologically inspired Temporal Noisy-Leak), Integrator (TNLI) neuron model, with partial reset. The complete randomness or high firing variability, can be achieved for certain input parameter values at high firing rates, which results from the dendritic temporal summation of postsynaptic responses and the use of random synaptic inputs. It is also demonstrated that bursting behaviour can indeed be achieved, using the TNLI, which is a result of the use of random synapses and distal inputs. The firing variability is demonstrated by calculating the Coefficient of Variation (Cv) of the interspike interval (ISI) distribution and by observing the corresponding ISI histograms. © 1995, Springer Verlag. All rights reserved. | en |
dc.source | 3rd International Workshop on Artificial Neural Networks, IWANN 1995 | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-84947426417&partnerID=40&md5=b9b9eaad03fef90d4ce7f3ca7ea01c89 | |
dc.subject | Statistical methods | en |
dc.subject | Neural networks | en |
dc.subject | Stochastic systems | en |
dc.subject | Biologically inspired | en |
dc.subject | Neurons | en |
dc.subject | Coefficient of variation | en |
dc.subject | Temporal summation | en |
dc.subject | Input parameter | en |
dc.subject | Interspike interval distributions | en |
dc.subject | Neuron model | en |
dc.subject | Partial resets | en |
dc.subject | Synaptic input | en |
dc.title | A review on the stochastic firing behaviour of real neurons and how it can be modelled | en |
dc.type | info:eu-repo/semantics/article | |
dc.description.volume | 930 | |
dc.description.startingpage | 223 | |
dc.description.endingpage | 230 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Article | en |
dc.description.notes | <p>Sponsors: CICYT, Spain | en |
dc.description.notes | DG-XII Human Capital and Mobility (EC) | en |
dc.description.notes | DGICYT (MEC) | en |
dc.description.notes | Junta de Andalucia | en |
dc.description.notes | Conference code: 145669</p> | en |
dc.source.abbreviation | Lect. Notes Comput. Sci. | en |
dc.contributor.orcid | Christodoulou, Chris C. [0000-0001-9398-5256] | |
dc.gnosis.orcid | 0000-0001-9398-5256 | |