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International Work-Conference on Artificial Neural Networks

IWANN 1997: Biological and Artificial Computation: From Neuroscience to Technology pp 433–442Cite as

Stability and hebbian learning in populations of probabilistic neurons

  • Plasticity Phenomena (Maturing, Learning and Memory)
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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1240))

Abstract

The effect of a hebbian learning process in an isolated population of neurons is investigated using numerical simulations on a probabilistic neural network model. An increase of regularity in spike production is observed as a result of exposure to messages received by connections of adapting strength. The simple mechanism of synaptic adaptation that uses local information available at synapses is capable of driving the population towards a stable firing rhythm and does so by selecting a stable set of synaptic weights. The observed stable limit is coherent with a low firing profile in the activity of the isolated population model.

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Author information

Authors and Affiliations

  1. Instituto de Ingeniería del Conocimiento, Universidad Autónoma de Madrid, Canto Blanco, Mod. C-XVI, P.2, 28049, Madrid, Spain

    Francisco B. Rodríguez & Vicente López

Authors
  1. Francisco B. Rodríguez
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  2. Vicente López
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Editor information

José Mira Roberto Moreno-Díaz Joan Cabestany

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© 1997 Springer-Verlag Berlin Heidelberg

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Rodríguez, F.B., López, V. (1997). Stability and hebbian learning in populations of probabilistic neurons. In: Mira, J., Moreno-Díaz, R., Cabestany, J. (eds) Biological and Artificial Computation: From Neuroscience to Technology. IWANN 1997. Lecture Notes in Computer Science, vol 1240. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0032502

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  • DOI: https://doi.org/10.1007/BFb0032502

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63047-0

  • Online ISBN: 978-3-540-69074-0

  • eBook Packages: Springer Book Archive

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