Simulated generation of evoked potentials components using networks with distinct excitatory and inhibitory neurons

dc.contributor.author	Βεντούρας, Ερρίκος Μ.	el
dc.contributor.author	Ουζούνογλου, Νικόλαος Κ.	el
dc.contributor.author	Κουτσουρής, Δημήτριος Γ.	el
dc.contributor.author	Παπαγεωργίου, Χαράλαμπος	el
dc.contributor.author	Ραμπαβίλας, Ανδρέας Ν.	el
dc.date.accessioned	2015-02-02T09:18:42Z
dc.date.available	2015-02-02T09:18:42Z
dc.date.issued	2015-02-02
dc.identifier.uri	http://hdl.handle.net/11400/5432
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.subject	Bioelectric potentials
dc.subject	Brain--Models
dc.subject	Βιοηλεκτρικό δυναμικό
dc.subject	Μοντέλα εγκεφάλου
dc.title	Simulated generation of evoked potentials components using networks with distinct excitatory and inhibitory neurons	en
heal.type	journalArticle
heal.classification	Medicine
heal.classification	Medical technology
heal.classification	Ιατρική
heal.classification	Ιατρικά όργανα και εξοπλισμός
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh00006614
heal.classificationURI	http://skos.um.es/unescothes/C02465
heal.classificationURI	N/A-Ιατρική
heal.classificationURI	N/A-Ιατρικά όργανα και εξοπλισμός
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh85016344
heal.contributorName	Στεφανής, Κωνσταντίνος Ν.	el
heal.identifier.secondary	DOI: 10.1109/4233.870034
heal.language	en
heal.access	campus
heal.recordProvider	Τ.Ε.Ι. Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Βιοϊατρικής Τεχνολογίας Τ.Ε.	el
heal.publicationDate	2000
heal.bibliographicCitation	Ventouras, E., Uzunoglu, N., Koutsouris, D., papageorgiou, C., Rabavilas, A., et al. (September 2000). Simulated generation of evoked potentials components using networks with distinct excitatory and inhibitory neurons. IEEE Transactions on Information Technology in Biomedicine. 4(3). pp.238-246. IEEE.	en
heal.abstract	Long latency evoked potentials (EPs) are electrical potentials related to brain information processing mechanisms. A three-layered neurophysiologically based artificial neural network model is presented whose neurons obey to Dale's law. The first two layers of the network can memorize and recall sparsely coded patterns, oscillating at biologically plausible frequencies. Excitatory low-pass filtering synapses, from the second to the third layer, create evoked current dipoles, when the network retrieves memories related to stimuli. Based on psychophysiological indications, simulated intracranial dipoles are straightforwardly transformed into long latency EP components such as N/sub 100/ and P/sub 300/ that match laboratory-measured scalp EPs.	en
heal.sponsor	IEEE Computer Society [Technical Co-Sponsor]	en
heal.publisher	IEEE	el
heal.journalName	IEEE Transactions on Information Technology in Biomedicine	el
heal.journalType	peer-reviewed
heal.fullTextAvailability	true