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Percolation and Frustration in Neural Networks

  • A. J. Noest
Conference paper
  • 168 Downloads
Part of the NATO ASI Series book series (volume 20)

Abstract

There are reasons to believe that the theoretical and computational techniques of solid state physics can be used to get an understanding of the dynamics of the brain. Here I shall approach along similar lines a related, but less ambitious set of problems stemming from tissue-culture experiments with foetal rat cortex neurons [1]. Starting from a planar array of disconnected cells, dendrites and axons grow out slowly, leading via the formation of clusters of connected cells to a percolating network. After a week, synapses develop at the contacts, allowing the cells to transmit spike-signals by a discrete stochastic process. Growth of the network is much slower than spike-propagation. Two main cell types exist: excitatory cells that increase the spike rate of their target cells, and inhibitory cells that decrease it.

Keywords

Critical Exponent Universality Class Inhibitory Cell Spike Rate Spacetime Structure 
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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Copyright information

© Springer-Verlag Berlin Heidelberg 1986

Authors and Affiliations

  • A. J. Noest
    • 1
  1. 1.Netherlands Institute for Brain ResearchAmsterdamThe Netherlands

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