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

Subscribe (Full Service)


	Search: The ACM Digital Library The Guide

	Feedback

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

Full text

Pdf (1.32 MB)

Source

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

Publisher

ACM New York, NY, USA

Bibliometrics

Downloads (6 Weeks): 5, Downloads (12 Months): 28, Citation Count: 3

Additional Information:

references cited by index terms collaborative colleagues

Tools and Actions:	Review this Article Save this Article to a Binder Display Formats: BibTeX EndNote ACM Ref

DOI Bookmark:	Use this link to bookmark this Article: http://doi.acm.org/10.1145/267460.267477 What is a DOI?

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.

	1	H. Agmon-Snir and I. Segev. Signal delay and input synchronization in passive dendritic structures. Journal of Neurophysioiogy, 70:2066-2085, 1993.
	2	Martin Anthony , Norman Biggs, Computational learning theory: an introduction, Cambridge University Press, New York, NY, 1992
	3	Avrim L. Blum , Ronald L. Rivest, Training a 3-node neural network is NP-complete, Neural Networks, v.5 n.1, p.117-127, 1992 [doi>10.1016/S0893-6080(05)80010-3]
	4	Anselm Blumer , A. Ehrenfeucht , David Haussler , Manfred K. Warmuth, Learnability and the Vapnik-Chervonenkis dimension, Journal of the ACM (JACM), v.36 n.4, p.929-965, Oct. 1989 [doi>10.1145/76359.76371]
	5	T. M. Cover. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Transactions on Electronic Computers, 14:326-334, 1965.
	6	Michael R. Garey , David S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness, W. H. Freeman & Co., New York, NY, 1979
	7	W. Gerstner. Time structure of the activity in neural network models. Phys. Rev. E, 51:738-758, 1995.
	8	W. Gerstner, R. Kempter, J. L. van Hemmen, and H. Wagner. A neuronal learning rule for submillisecond temporal coding. Nature, 383:76-78, 1996.
	9	David Haussler, Decision theoretic generalizations of the PAC model for neural net and other learning applications, Information and Computation, v.100 n.1, p.78-150, Sept. 1992 [doi>10.1016/0890-5401(92)90010-D]
	10	Klaus-U. Höffgen , Hans-U. Simon , Kevin S. van Horn, Robust trainability of single neurons, Journal of Computer and System Sciences, v.50 n.1, p.114-125, Feb. 1995 [doi>10.1006/jcss.1995.1011]
	11	Michael Kearns , Ming Li , Leslie Valiant, Learning Boolean formulas, Journal of the ACM (JACM), v.41 n.6, p.1298-1328, Nov. 1994 [doi>10.1145/195613.195656]
	12	Michael J. Kearns , Robert E. Schapire , Linda M. Sellie, Toward efficient agnostic learning, Proceedings of the fifth annual workshop on Computational learning theory, p.341-352, July 27-29, 1992, Pittsburgh, Pennsylvania, United States [doi>10.1145/130385.130424]
	13	Wolfgang Maass, Fast sigmoidal networks via spiking neurons, Neural Computation, v.9 n.2, p.279-304, Feb. 15, 1997
	14	W. Maass. Networks of spiking neurons: the third generation of neural network models. Neural Networks, to appear; extended abstract in Proceedings of the Seventh Australian Conference on Neural Networks, Canberra, 1996, pp. 1-10.
	15	S. Muroga. Threshold Logic and Its Applications. John Wiley & Sons, New York, 1971.
	16	S. Muroga and I. Toda. Lower bound of the number of threshold functions. IEEE Transactions on Electronic Computers, 15:805-806, 1966.
	17	Alan F. Murray , Lionel Tarassenko, Analogue Neural VLSI: A Pulse Stream Approach, Chapman & Hall, Ltd., London, UK, 1994
	18	L. Schlafli. Gesammelte Mathematische Abhandlungen I. Birkhauser, Basel, 1950, pp. 209-212.
	19	H. C. Tuckwell. Introduction to Theoretical Neurobiology, vols. 1 and 2. Cambridge University Press, Cambridge, 1988.
	20	L. G. Valiant, A theory of the learnable, Communications of the ACM, v.27 n.11, p.1134-1142, Nov. 1984 [doi>10.1145/1968.1972]
	21	Anthony M. Zador , Barak A. Pearlmutter, VC dimension of an integrate-and-fire neuron model, Neural Computation, v.8 n.3, p.611-624, April 1996

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