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Slow stochastic Hebbian learning of classes of stimuli in a recurrent neural network.

Publication ,  Journal Article
Brunel, N; Carusi, F; Fusi, S
Published in: Network
February 1998

We study unsupervised Hebbian learning in a recurrent network in which synapses have a finite number of stable states. Stimuli received by the network are drawn at random at each presentation from a set of classes. Each class is defined as a cluster in stimulus space, centred on the class prototype. The presentation protocol is chosen to mimic the protocols of visual memory experiments in which a set of stimuli is presented repeatedly in a random way. The statistics of the input stream may be stationary, or changing. Each stimulus induces, in a stochastic way, transitions between stable synaptic states. Learning dynamics is studied analytically in the slow learning limit, in which a given stimulus has to be presented many times before it is memorized, i.e. before synaptic modifications enable a pattern of activity correlated with the stimulus to become an attractor of the recurrent network. We show that in this limit the synaptic matrix becomes more correlated with the class prototypes than with any of the instances of the class. We also show that the number of classes that can be learned increases sharply when the coding level decreases, and determine the speeds of learning and forgetting of classes in the case of changes in the statistics of the input stream.

Duke Scholars

Published In

Network

ISSN

0954-898X

Publication Date

February 1998

Volume

9

Issue

1

Start / End Page

123 / 152

Location

England

Related Subject Headings

  • Time Factors
  • Stochastic Processes
  • Pattern Recognition, Visual
  • Neurology & Neurosurgery
  • Neural Networks, Computer
  • Generalization, Stimulus
  • 4601 Applied computing
  • 3209 Neurosciences
  • 3202 Clinical sciences
  • 17 Psychology and Cognitive Sciences
 

Citation

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MLA
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Brunel, N., Carusi, F., & Fusi, S. (1998). Slow stochastic Hebbian learning of classes of stimuli in a recurrent neural network. Network, 9(1), 123–152.
Brunel, N., F. Carusi, and S. Fusi. “Slow stochastic Hebbian learning of classes of stimuli in a recurrent neural network.Network 9, no. 1 (February 1998): 123–52.
Brunel N, Carusi F, Fusi S. Slow stochastic Hebbian learning of classes of stimuli in a recurrent neural network. Network. 1998 Feb;9(1):123–52.
Brunel, N., et al. “Slow stochastic Hebbian learning of classes of stimuli in a recurrent neural network.Network, vol. 9, no. 1, Feb. 1998, pp. 123–52.
Brunel N, Carusi F, Fusi S. Slow stochastic Hebbian learning of classes of stimuli in a recurrent neural network. Network. 1998 Feb;9(1):123–152.
Journal cover image

Published In

Network

ISSN

0954-898X

Publication Date

February 1998

Volume

9

Issue

1

Start / End Page

123 / 152

Location

England

Related Subject Headings

  • Time Factors
  • Stochastic Processes
  • Pattern Recognition, Visual
  • Neurology & Neurosurgery
  • Neural Networks, Computer
  • Generalization, Stimulus
  • 4601 Applied computing
  • 3209 Neurosciences
  • 3202 Clinical sciences
  • 17 Psychology and Cognitive Sciences