On the complexity of learning for a spiking neuron (extended abstract)

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On the complexity of learning for a spiking neuron (extended abstract)

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Annual Workshop on Computational Learning Theory archive
Proceedings of the tenth annual conference on Computational learning theory table of contents

Nashville, Tennessee, United States

Pages: 54 - 61

Year of Publication: 1997

ISBN:0-89791-891-6

Authors

Wolfgang Maass	Institute for Theoretical Computer Science, Technische Universität Graz, Klosterwiesgasse 32/2, A-8010, Graz, Austria
Michael Schmitt	Institute for Theoretical Computer Science, Technische Universität Graz, Klosterwiesgasse 32/2, A-8010, Graz, Austria

Sponsors

AT&T Labs :
SIGACT: ACM Special Interest Group on Algorithms and Computation Theory
SIGART: ACM Special Interest Group on Artificial Intelligence
Vanderbilt University : Vanderbilt University

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ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 16, Downloads (12 Months): 42, Citation Count: 3

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REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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CITED BY 3

	Wolfgang Maass, On the relevance of time in neural computation and learning, Theoretical Computer Science, v.261 n.1, p.157-178, 06/17/2001
	Michael Schmitt, On computing Boolean functions by a spiking neuron, Annals of Mathematics and Artificial Intelligence, v.24 n.1-4, p.181-191, 1998
	Hélène Paugam-Moisy , Régis Martinez , Samy Bengio, Delay learning and polychronization for reservoir computing, Neurocomputing, v.71 n.7-9, p.1143-1158, March, 2008