Result: Generalized Stochastic Spiking Neuron Model and Extended Spike Response Model in Spatial-Temporal Pulse Pattern Detection Task

Title:
Generalized Stochastic Spiking Neuron Model and Extended Spike Response Model in Spatial-Temporal Pulse Pattern Detection Task
Authors:
SINYAVSKIY, O. Y, KOBRIN, A. I
Source:
Optical memory & neural networks. 19(4):300-309
Publisher Information:
New York, NY; New York, NY: Allerton Press, Springer, 2010.
Publication Year:
2010
Physical Description:
print, 11 ref
Original Material:
INIST-CNRS
Subject Terms:
Optics, Optique, Sciences exactes et technologie, Exact sciences and technology, Physique, Physics, Generalites, General, Instruments, appareillage, composants et techniques communs à plusieurs branches de la physique et de l'astronomie, Instruments, apparatus, components and techniques common to several branches of physics and astronomy, Informatique en physique expérimentale, Computers in experimental physics, Modélisation et simulation par ordinateur, Computer modeling and simulation, Apprentissage, Learning, Approche probabiliste, Probabilistic approach, Enfoque probabilista, Détection signal, Signal detection, Extraction caractéristique, Feature extraction, Impulsion, Linear momentum, Loi probabilité, Probability distribution, Ley probabilidad, Méthode entropie minimum, Minimum entropy methods, Neurone impulsionnel, Spiking neuron, Neurona pulsante, Opérateur évolution, Evolution operator, Operador evolución, Temps retard, Time delay, Théorie information, Information theory, 0705T, information entropy, information theory, neuron models, spiking neuron learning, spiking neuron
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Moscow Power Engineering Institute (TU), Krasnokazarmennaya st. 14, Moscow, 111250, Russian Federation
ISSN:
1060-992X
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=23885657
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Metrology
Accession Number:
edscal.23885657
Database:
PASCAL Archive

Further Information

A generalized model of spiking neuron is proposed as a non-stationary stochastic spike sequences processing unit. The generalized spiking neuron model is described with a conditional spike generation probability distribution and with a state evolution operator. An information theory language is used for the convenient neuron's learning tasks description. The problem of spiking neuron learning with the teacher is solved using information entropy minimization algoritlun. A particular implementation of generalized model using stochastic Spike Response Model with alpha-functions set is provided. A task of time delay maintenance between input and output spikes and a task of detecting of a spiking pattern in a noisy stream of pulse signals are considered using extended SRM neuron. It is shown that after the using of the proposed learning method spiking neuron became capable to detect a spatial-temporal pulse pattern and to serve as an adaptive delay unit.