Role of Itinerancy Among Attractors as Dynamical Map in Distributed Coding Scheme

A basic frame, based on which certain features of sensory stimuli can be extracted systematically in a distributed coding scheme, has not been clarified yet. This paper proposes that the basic frame can be a dynamical map represented by itinerancy among attractors. Features of entities are encoded b...

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Published in:	Neural networks Vol. 10; no. 8; pp. 1375 - 1390
Main Authors:	Hoshino, Osamu, Usuba, Noriaki, Kashimori, Yoshiki, Kambara, Takeshi
Format:	Journal Article
Language:	English
Published:	Oxford Elsevier Ltd 01.11.1997 Elsevier Science
Subjects:	Applied sciences Artificial intelligence Chaotic itinerancy Computer science; control theory; systems Connectionism. Neural networks Distributed coding Dynamical map Exact sciences and technology Memorization under theta rhythm Phase transition Short-term synaptic change Distributed coding Chaotic itinerancy Dynamical map Memorization under theta rhythm Short-term synaptic change Phase transition Self organization Dynamic characteristic Coding Information processing Chaotic attractor Information extraction Neural network Phase transitions Recognition
ISSN:	0893-6080, 1879-2782, 1879-2782
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Abstract	A basic frame, based on which certain features of sensory stimuli can be extracted systematically in a distributed coding scheme, has not been clarified yet. This paper proposes that the basic frame can be a dynamical map represented by itinerancy among attractors. Features of entities are encoded by attractors of neural networks. Relations between the features in each modality are mapped on dynamical links between the attractors in each network relevant to each modality. The itinerant states, in which the network dynamic state itinerates chaotically or cyclically among the attractors, can work as dynamical maps. The recognition of features is carried out by a phase transition from an itinerant state to a constituent attractor. The phase transition is induced by a short-term synaptic change based on the Hebbian rule under application of a relevant stimulation. A theta-like global oscillation is necessary for self-organized formation of the chaotically itinerant state.
AbstractList	A basic frame, based on which certain features of sensory stimuli can be extracted systematically in a distributed coding scheme, has not been clarified yet. This paper proposes that the basic frame can be a dynamical map represented by itinerancy among attractors. Features of entities are encoded by attractors of neural networks. Relations between the features in each modality are mapped on dynamical links between the attractors in each network relevant to each modality. The itinerant states, in which the network dynamic state itinerates chaotically or cyclically among the attractors, can work as dynamical maps. The recognition of features is carried out by a phase transition from an itinerant state to a constituent attractor. The phase transition is induced by a short-term synaptic change based on the Hebbian rule under application of a relevant stimulation. A theta-like global oscillation is necessary for self-organized formation of the chaotically itinerant state. A basic frame, based on which certain features of sensory stimuli can be extracted systematically in a distributed coding scheme, has not been clarified yet. This paper proposes that the basic frame can be a dynamical map represented by itinerancy among attractors. Features of entities are encoded by attractors of neural networks. Relations between the features in each modality are mapped on dynamical links between the attractors in each network relevant to each modality. The itinerant states, in which the network dynamic state itinerates chaotically or cyclically among the attractors, can work as dynamical maps. The recognition of features is carried out by a phase transition from an itinerant state to a constituent attractor. The phase transition is induced by a short-term synaptic change based on the Hebbian rule under application of a relevant stimulation. A theta-like global oscillation is necessary for self-organized formation of the chaotically itinerant state.A basic frame, based on which certain features of sensory stimuli can be extracted systematically in a distributed coding scheme, has not been clarified yet. This paper proposes that the basic frame can be a dynamical map represented by itinerancy among attractors. Features of entities are encoded by attractors of neural networks. Relations between the features in each modality are mapped on dynamical links between the attractors in each network relevant to each modality. The itinerant states, in which the network dynamic state itinerates chaotically or cyclically among the attractors, can work as dynamical maps. The recognition of features is carried out by a phase transition from an itinerant state to a constituent attractor. The phase transition is induced by a short-term synaptic change based on the Hebbian rule under application of a relevant stimulation. A theta-like global oscillation is necessary for self-organized formation of the chaotically itinerant state.
Author	Kambara, Takeshi Usuba, Noriaki Kashimori, Yoshiki Hoshino, Osamu
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Keywords	Distributed coding Chaotic itinerancy Dynamical map Memorization under theta rhythm Short-term synaptic change Phase transition Self organization Dynamic characteristic Coding Information processing Chaotic attractor Information extraction Neural network Phase transitions Recognition
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Snippet	A basic frame, based on which certain features of sensory stimuli can be extracted systematically in a distributed coding scheme, has not been clarified yet....
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SubjectTerms	Applied sciences Artificial intelligence Chaotic itinerancy Computer science; control theory; systems Connectionism. Neural networks Distributed coding Dynamical map Exact sciences and technology Memorization under theta rhythm Phase transition Short-term synaptic change
Title	Role of Itinerancy Among Attractors as Dynamical Map in Distributed Coding Scheme
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