The role of constraints in Hebbian learning
Neural Computation
A Role of Constraint in Self-Organization
RANDOM '98 Proceedings of the Second International Workshop on Randomization and Approximation Techniques in Computer Science
New Applications of the Incompressibility Method
ICAL '99 Proceedings of the 26th International Colloquium on Automata, Languages and Programming
A Role of Constraint in Self-Organization
RANDOM '98 Proceedings of the Second International Workshop on Randomization and Approximation Techniques in Computer Science
APPROX '01/RANDOM '01 Proceedings of the 4th International Workshop on Approximation Algorithms for Combinatorial Optimization Problems and 5th International Workshop on Randomization and Approximation Techniques in Computer Science: Approximation, Randomization and Combinatorial Optimization
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In this paper we study a neural network model of self-organization. This model uses a variation of a 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 abstracts 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.