Number of the records: 1

Gradient Learning in Networks of Smoothly Spiking Neurons

SYSNO ASEP	0318366
Document Type	C - Proceedings Paper (int. conf.)
R&D Document Type	Conference Paper
Title	Gradient Learning in Networks of Smoothly Spiking Neurons
Title	Gradientní učení sítí hladce pulzních neuronů
Author(s)	Šíma, Jiří (UIVT-O)_{RID, SAI, ORCID}
Source Title	Advances in Neuro-Information Processing. Revised Selected Papers Part II. - Berlin : Springer, 2009 / Köppen M. ; Kasabov N. ; Coghill G. - ISBN 978-3-642-03039-0
Pages	s. 179-186
Number of pages	8 s.
Action	ICONIP 2008. International Conference on Neural Information Processing /15./
Event date	25.11.2008-28.11.2008
VEvent location	Auckland
Country	NZ - New Zealand
Event type	WRD
Language	eng - English
Country	DE - Germany
Keywords	spiking neuron ; back-propagation ; SpikeProp ; gradient learning
Subject RIV	IN - Informatics, Computer Science
R&D Projects	1ET100300517 GA AV ČR - Academy of Sciences of the Czech Republic (AV ČR)
	1M0545 GA MŠMT - Ministry of Education, Youth and Sports (MEYS)
CEZ	AV0Z10300504 - UIVT-O (2005-2011)
UT WOS	000270578200022
EID SCOPUS	70349106368
DOI	10.1007/978-3-642-03040-6_22
Annotation	A slightly simplified version of the Spike Response Model SRM0 of a spiking neuron is tailored to gradient learning. In particular, the evolution of spike trains along the weight and delay parameter trajectories is made perfectly smooth. For this model a back-propagation-like learning rule is derived which propagates the error also along the time axis. This approach overcomes the difficulties with the discontinuous-in-time nature of spiking neurons, which encounter previous gradient learning algorithms (e.g. SpikeProp). The new algorithm can naturally cope with multiple spikes and preliminary experiments confirm the smoothness of spike creation/deletion process.
Workplace	Institute of Computer Science
Contact	Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800
Year of Publishing	2010

Number of the records: 1