In this article is presented a very simple and effective analog spiking neural network simulator, realized with an event-driven method, taking into account a basic biological neuron parameter: the spike latency. Also, other fundamentals biological parameters are considered, such as subthreshold decay and refractory period. This model allows to synthesize neural groups able to carry out some substantial functions. The proposed simulator is applied to elementary structures, in which some properties and interesting applications are discussed, such as the realization of a Spiking Neural Network Classifier.

Salerno, M., Susi, G., Cristini, A., Sanfelice, Y., & D’Annessa, A. (2013). Spiking neural networks as continuous-time dynamical systems: fundamentals, elementary structures and simple applications. ACEEE INTERNATIONAL JOURNAL ON INFORMATION TECHNOLOGY, 3(1), 80-89.

Spiking neural networks as continuous-time dynamical systems: fundamentals, elementary structures and simple applications

SALERNO, MARIO;SUSI, GIANLUCA;
2013

Abstract

In this article is presented a very simple and effective analog spiking neural network simulator, realized with an event-driven method, taking into account a basic biological neuron parameter: the spike latency. Also, other fundamentals biological parameters are considered, such as subthreshold decay and refractory period. This model allows to synthesize neural groups able to carry out some substantial functions. The proposed simulator is applied to elementary structures, in which some properties and interesting applications are discussed, such as the realization of a Spiking Neural Network Classifier.
Pubblicato
Rilevanza internazionale
Articolo
Sì, ma tipo non specificato
Settore ING-IND/31 - Elettrotecnica
Settore ING-INF/01 - Elettronica
Settore ING-INF/06 - Bioingegneria Elettronica e Informatica
eng
Neuron; Spiking Neural Network (SNN); Latency; Event-Driven, Plasticity; Threshold; Neuronal Group Selection; SNN classifier
http://doi.searchdl.org/01.IJIT.3.1.1129
Salerno, M., Susi, G., Cristini, A., Sanfelice, Y., & D’Annessa, A. (2013). Spiking neural networks as continuous-time dynamical systems: fundamentals, elementary structures and simple applications. ACEEE INTERNATIONAL JOURNAL ON INFORMATION TECHNOLOGY, 3(1), 80-89.
Salerno, M; Susi, G; Cristini, A; Sanfelice, Y; D’Annessa, A
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2108/110768
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