Abstract
The human brain classifies natural scenes and recognizes objects in complex visual patterns with a high precision in a minimum amount of processing time. Only few action potentials (spikes) per neuron and per processing stage are sufficient to achieve this astonishingly high performance, despite the random nature of the incoming spike trains. In this contribution, we present a novel algorithm which updates the internal representation of patterns in a generative model with each incoming spike. We first demonstrate that our algorithm is capable of learning a suitable representation of pattern ensembles from stochastically generated spike trains. This representation is then used for classifying test patterns, requiring less than one spike per input node to achieve a performance comparable to standard algorithms in pattern recognition.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Bethge, M., Rotermund, D., Pawelzik, K.: Optimal short-term population coding: When Fisher information fails. Neural Computation 14(10), 2317–2351 (2002); A second order phase transition in neural rate coding: Binary encoding is optimal for rapid signal transmission. Phys. Rev. Lett. 90, 88104 (2002)
Gerstner, W.: Population Dynamics of Spiking Neurons: Fast Transients, Asynchronous States, and Locking. Neural Computation 12, 43–89 (2000)
Lanteri, H., Roche, M., Aime, C.: Penalized maximum likelihood image restoration with positivity constraints: multiplicative algorithms. Inverse Problems 18, 1397–1419 (2002)
Lee, D.D., Seung, S.H.: Learning the parts of objects by non-negative matrix factorization. Nature 401, 788–791 (1999)
Pawelzik, K.R., Rotermund, D., Ernst, U.A.: Building representations spike by spike. In: Elsner, N., Zimmermann, H. (eds.) Proceedings of the 29th Göttingen Neurobiology Conference, p. 1041. Georg Thieme Verlag, Stuttgart (2003)
Pawelzik, K.R., Ernst, U.A., Trenner, D., Rotermund, D.: Building representations spike by spike. In: Proceedings of the Society of Neuroscience Conference 2002, Orlando, p. 557.12 (2002)
Schölkopf, B.: Support Vector Learning, R. Oldenbourg Verlag, München (1997), http://www.kernel-machines.org/papers/book_ref.ps.gz
Thorpe, S., Fize, D., Marlot, C.: Speed of processing in the human visual system. Nature 381, 520–522 (1996)
Thorpe, S., Delorme, A., van Rullen, R.: Spike-based strategies for rapid processing. Neural Networks 14, 521–525 (2001)
Wiener, M., Richmond, B.J.: Decoding spike trains instant by instant using order statistics and the mixture-of-Poissons model. J. Neurosci. 23, 2394–2406 (2003)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ernst, U.A., Rotermund, D., Pawelzik, K.R. (2004). An Algorithm for Fast Pattern Recognition with Random Spikes. In: Rasmussen, C.E., Bülthoff, H.H., Schölkopf, B., Giese, M.A. (eds) Pattern Recognition. DAGM 2004. Lecture Notes in Computer Science, vol 3175. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28649-3_49
Download citation
DOI: https://doi.org/10.1007/978-3-540-28649-3_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22945-2
Online ISBN: 978-3-540-28649-3
eBook Packages: Springer Book Archive