Presentation 1998/10/24
SIMILARITY BETWEEN CHAOS AND SCHRODINGER EQUATION AND CALCULATION PRECISION OF PROBABILISTIC DENSITY DISTRIBUTION OF ORBIT IN HIERARCHICAL NEURAL NETWORK
Mitsuo TAKASE,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) It is known that chaos is produced in brain and neural networks. Chaos can work functionally changing attractors, especially consistently with psychological mechanism. On the other hand, chaos behaves like quantum mechanics in neural network and it is known that chaos can be expressed by Schrodinger equation. I think about how we can make chaos in neural network correspond to Schrodinger equation and I also think about the meanings. When we analyze chaos through Schrodinger equation instead of attractor in dynamical system, we can treat not only the scaly and the shape of attractor, but also the probabilistic density function in the attractor. Especially an eigenvalue of loop connections with feedback, which depends on the slope of input-output function of excitatory cells and synapse conductivities in neural network, determines the precision of the probabilistic density distribution. In some cases where there is possibility for a part of brain to be able to monitor the precision of the probabilistic density distribution, the precision can become the valuable information of reliability to judge in the cases.
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Keyword(in English) Schrodinger equation / chaos / probabilistic density distribution / neural network
Paper # NC98-53
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Conference Information
Committee NC
Conference Date 1998/10/24(1days)
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Registration To Neurocomputing (NC)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) SIMILARITY BETWEEN CHAOS AND SCHRODINGER EQUATION AND CALCULATION PRECISION OF PROBABILISTIC DENSITY DISTRIBUTION OF ORBIT IN HIERARCHICAL NEURAL NETWORK
Sub Title (in English)
Keyword(1) Schrodinger equation
Keyword(2) chaos
Keyword(3) probabilistic density distribution
Keyword(4) neural network
1st Author's Name Mitsuo TAKASE
1st Author's Affiliation ()
Date 1998/10/24
Paper # NC98-53
Volume (vol) vol.98
Number (no) 365
Page pp.pp.-
#Pages 4
Date of Issue