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					ICANN 2006, Greece




  Backbone Structure of Hairy
  Memory


                 Cheng-Yuan Liou
                 Department of Computer Science
                      and Information Engineering
                 National Taiwan University





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Discussions
   Patterns in {N_i,p & N_i,n} are backbones of the Hopfield
    model. They form the backbone structure of the model.

   Hairy model is a homeostatic system.

   All four methods, et-AM, e-AM, g-AM, and b-AM, derive
    asymmetric weight matrices with nonzero diagonal elements
    and keep Hebb’s postulate.

   In almost all of our simulations, the evolution of states
    converged in a single iteration (basin-1) during recall after
    learning. This is very different from the evolutionary recall
    process in many other models.


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Discussions
 All three methods, et-AM, e-AM, and g-AM, operate in one shift.
  Each hyperplane is adjusted in turn.
  Each iteration improves the location of a single hyperplane.
  Each hyperplane is independent of all others during learning.

 Localizing neuron damages
 Localizing learning

 The computational cost is linearly proportional to the network
  size, N, and the number of patterns, P.




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Discussions
   All of the methods, et-AM, e-AM, g-AM and b-AM
    give non-zero values to the self-connections, wii \=
    0, which is very different from Hopfield’s setting, wii
    = 0.
   We are still attempting to understand and clarify the
    meaning of the setting wii = 0, where newborn
    neurons start learning from full self-reference, wii =
    1, and end with whole network-reference, wii = 0.
   This is beneficial for cultured neurons working as a
    whole. This implies that stabilizing memory might
    not be the only purpose of learning and evolution

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Discussions
   The Boltzmann machine can be designed
    according to et-AM, e-AM, or g-AM.




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