An Interruptible Connectionist Model for Real-Time Pattern Recognition

Muller, Jean-Denis

doi:10.1007/978-1-4471-1599-1_158

Icann 98

1998

DOI: 10.1007/978-1-4471-1599-1_158

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An Interruptible Connectionist Model for Real-Time Pattern Recognition

Jean-Denis Muller

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Pattern recognition with spiking neurons: performance enhancement based on a statistical analysis

Godin

Muller²,

Gordon³

et al.

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)

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1 2 29 3, (godin@bruyeres.cea.fr), J. D. Muller ( l ) (muller@ldg.bruyeres.cea.fi), M. B. Gordon (2)(gordon@drfmc.ceng.cea.fi) and J. Haussy (')(hausy@bruyeres.cea.fi) Abstract PCIW (Pulse Coupled Neural Networh) and more generally spiking-neuron models seem to meet the realtime and robustness constraints necessav in on-board pattern recognition applications. However, ejicient learning algorithms are still lacking for such networks. In this paper we consider a feedgoward network of spiking neurons. m e weights and bioses are obtained after a simple transfonnation of those learned with standard back-propagation on a static (stanah-4 neural network We discuss the conditions under which this transformationgives good recognition rates, in the case of handwritten digit recognition.

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Pattern recognition with spiking neurons: performance enhancement based on a statistical analysis

Godin

Muller²,

Gordon³

et al.

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339)

View full text Add to dashboard Cite

show abstract

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An Interruptible Connectionist Model for Real-Time Pattern Recognition

Cited by 1 publication

References 5 publications

Pattern recognition with spiking neurons: performance enhancement based on a statistical analysis

Pattern recognition with spiking neurons: performance enhancement based on a statistical analysis

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