TR on the Modelling of Synaptic Plasticity

Patrick Thomas thomasp at informatik.tu-muenchen.dbp.de
Thu Dec 27 07:27:38 EST 1990


The following technical report is now available:


                             BEYOND HEBB SYNAPSES:
             BIOLOGICAL BUILDING BLOCKS FOR UNSUPERVISED LEARNING
                         IN ARTIFICIAL NEURAL NETWORKS

                               Patrick V. Thomas

                               Report FKI-140-90

                                   Abstract

  This paper briefly reviews the neurobiology of synaptic plasticity as
  it is related to the formulation of learning rules for unsupervised
  learning in artificial neural networks. Presynaptic, postsynaptic and
  heterocellular mechanisms are discussed and their relevance to neural
  modelling is assessed. These include a variety of phenomena of potentiation
  as well as depression with time courses of action ranging from milliseconds
  to weeks. The original notion put forward by Donald Hebb stating that
  synaptic plasticity depends on correlated pre- and postsynaptic firing
  is reportedly inadequate. Although postsynaptic depolarization is necessary
  for associative changes in synaptic strength to take place (which conforms
  to the spirit of the hebbian law) the association is understood as being
  formed between pathways converging on the same postsynaptic neuron. The
  latter only serves as a supporting device carrying signals between activated
  dendritic regions and maintaining long-term changes through molecular
  mechanisms. It is further proposed to restrict the interactions of
  synaptic inputs to distinct compartments. The hebbian idea that the state
  of the postsynaptic neuron as a whole governs the sign and magnitude of
  changes at individual synapses is dropped in favor of local mechanisms
  which guide the depolarization-dependent associative learning process
  within dendritic compartments. Finally, a framework for the modelling of
  associative and non-associative mechanisms of synaptic plasticity at
  an intermediate level of abstraction, the Patchy Model Neuron, is sketched.


To obtain a copy of the technical report FKI-140-90 please send your physical
mail address to either "thomasp at lan.informatik.tu-muenchen.de" or Patrick V.
Thomas, Institute for Medical Psychology, Goethe-31, 8000 Munich 2, Germany.









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