On-Line Real-Time Oriented Application for Neuronal Spike Sorting with Unsupervised Learning.

Détails

ID Serval
serval:BIB_36139
Type
Actes de conférence (partie): contribution originale à la littérature scientifique, publiée à l'occasion de conférences scientifiques, dans un ouvrage de compte-rendu (proceedings), ou dans l'édition spéciale d'un journal reconnu (conference proceedings).
Collection
Publications
Institution
Titre
On-Line Real-Time Oriented Application for Neuronal Spike Sorting with Unsupervised Learning.
Titre de la conférence
Artificial Neural Networks: Biological Inspirations – ICANN 2005: 15th International Conference, Warsaw, Poland, September 11-15, 2005. Proceedings, Part I
Auteur⸱e⸱s
Asai Y., Aksenova T.I., Villa A.E.P.
Editeur
Springer
Adresse
Warsaw, Poland
ISBN
978-3-540-28754-4
978-3-540-28752-0
Statut éditorial
Publié
Date de publication
2005
Peer-reviewed
Oui
Editeur⸱rice scientifique
Duch W., Kacprzyk J., Oja E., Zadrożny S.
Volume
3696
Série
Lecture Notes in Computer Science
Pages
109-114
Langue
anglais
Résumé
Multisite electrophysiological recordings have become a standard tool for exploring brain functions. These techniques point out the necessity of fast and reliable unsupervised spike sorting. We present an algorithm that performs on-line real-time spike sorting for data streaming from a data acquisition board or in off-line mode from a WAV formatted file. Spike shapes are represented in a phase space according to the first and second derivatives of the signal trace. The output of the application is spike data format file in which the timing of spike occurrences are recorded by their inter-spike-intervals. It allows its application to the study of neuronal activity patterns in clinically recorded data.
Web of science
Création de la notice
19/11/2007 11:10
Dernière modification de la notice
20/08/2019 14:23
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