| Project/Area Number |
08044171
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| Research Category |
Grant-in-Aid for international Scientific Research
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| Allocation Type | Single-year Grants |
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| Section | Joint Research |
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| Research Institution | Tokyo Denki University |
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Principal Investigator |
HORIO Yoshihiko Tokyo Denki University Associate Professor, 工学部, 助教授 (60199544)
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| Co-Investigator(Kenkyū-buntansha) |
SUYAMA Ken The University of Tokyo Associate Professor, 電気工学科, 助教授
AIHARA Kazuyuki The University of Tokyo Associate Professor, 工学部, 助教授 (40167218)
KEN Suyama コロンビア大学, 電気工学科, 助教授
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| Project Period (FY) |
1996
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| Project Status |
Completed (Fiscal Year 1996)
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| Budget Amount *help |
¥1,600,000 (Direct Cost: ¥1,600,000)
Fiscal Year 1996: ¥1,600,000 (Direct Cost: ¥1,600,000)
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| Keywords | Chaos / Neural Network / Complex System / Analog VLSI / Nonlinear System / Dynamical System |
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| Research Abstract |
A chaotic neuron model has been implemented as an integrated circuit (IC) form using switched-capacitor circuit technique. The model includes a relative refractoriness and an analog output function of the axon hill-lock which are important characteristics of biological neurons. The integrated chaotic neuron circuit has current input nodes in order to add many input currents easily. A current to voltage converter circuit was specially designed for this purpose.
Furthermore, a synaptic circuit has been integrated as an IC form in order to construct a large scale chaotic neural network by connecting many neuron circuits. Within the synaptic chip, many current-mode synapse circuits and analog memories for synaptic weights are integrated. The analog memories are periodically refreshed by a master current copier circuit.
By using the above neuron chips and synaptic chips, a small-scale but full-functional chaotic neural network has been constructed. The network characteristics have been tested using the following three basic problems ;
1.Dynamical Associative Memory : From experiments, a dynamical retrieval of memories like animals do has been confirmed.
2.Optimization Problems : As an example, a traveling salesman problem has been solved using the network. As a result, very high solving ability of the network has been confirmed.
3.Synchronization of Chaos : synchronous motion of some chaos in the network has been achieved.
The behavior of the implemented chaotic neural network system can be observed in real-time. Therefore, it is possible to analyze the process of information processing of a coupled chaotic system from the view point of dynamics theory or information theory by constructing a larger network. The expansion of the size of the network and construction of a multi-order parallel observation system are future problems.
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