In this paper we introduce a neural network model of
self-organization. This model uses a variation of Hebb rule for
updating its synaptic weights, and surely converges to the equilibrium
status. The key point of the convergence is the update rule that
constrains the total synaptic weight and this seems to make the model
stable. We investigate the role of the constraint and show that it is
the constraint that makes the model stable. For analyzing this
setting, we propose a simple probabilistic game that models the neural
network and the self-organization process. Then, we investigate the
characteristics of this game, namely, the probability that the game
becomes stable and the number of the steps it takes.
CitationDomingo, C., Watanabe, O., Yamazaki, T. "A Role of constraint in self-organization". 1998.
All rights reserved. This work is protected by the corresponding intellectual and industrial property rights. Without prejudice to any existing legal exemptions, reproduction, distribution, public communication or transformation of this work are prohibited without permission of the copyright holder. If you wish to make any use of the work not provided for in the law, please contact: firstname.lastname@example.org