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A Physiological Neural Network as an Autoassociative Memory

  • J. Buhmann
  • K. Schulten
Conference paper
  • 167 Downloads
Part of the NATO ASI Series book series (volume 20)

Abstract

We consider a neural network model in which the single neurons are chosen to resemble closely known physiological properties. The neurons are assumed to be linked by synapses which change their strength according to Hebbian rules [1] on a short time scale (100ms) [2]. Each nerve cell receives input from a primary set of receptors, which offer learning and test patterns without changing their own properties. The activity of the neurons is interpreted as the output of the network (see Fig.1). The backward bended arrows in Fig.1 indicate the feed-back due to the effect of the neuron activity on the synaptic strengths Sik between neuron k and i in the neural network.

Keywords

Cell Potential Synaptic Strength Excitatory Synapse Postsynaptic Cell Hebbian Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. D.O. HEBB: Organization of Behaviour. Wiley 1949Google Scholar
  2. C.v.d. Malsburg: Int.Rep.81/2 Dept. Neurobiol. MPI f. Biophysikalische Chemie, Göttingen (1981)Google Scholar
  3. J. BUHMANN, K. SCHULTEN: to be submitted to Biol.Cybern.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1986

Authors and Affiliations

  • J. Buhmann
    • 1
  • K. Schulten
    • 1
  1. 1.Physik-DepartmentTechnische Universität MünchenGarchingFed. Rep. Germany

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