Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
9653512 | Neurocomputing | 2005 | 6 Pages |
Abstract
We report on stochastic evolutions of firing states through feedforward neural networks with Mexican-Hat-type connectivity. The variance in connectivity, which depends on the pre-synaptic neuron, generates a common noisy input to post-synaptic neurons. We develop a theory to describe the stochastic evolution of the localized synfire chain driven by a common noisy input. The development of a firing state through neural layers does not converge to a certain fixed point but keeps on fluctuating. Stationary firing states except for a non-firing state are lost, but an almost stationary distribution of firing state is observed.
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Kosuke Hamaguchi, Masato Okada, Michiko Yamana, Kazuyuki Aihara,