1993 Fiscal Year Final Research Report Summary
Functional dissection of Neuron Network by Identification Method for Non-linear Signal Processing in Informational Engineering
| Project/Area Number |
04640648
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| Research Category |
Grant-in-Aid for General Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Research Field |
動物発生・生理学
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| Research Institution | Hokkaido University |
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Principal Investigator |
SHIMOZAWA Tateo Hokkaido Univ., Res.Inst.Electron.Sci., Professor, 電子科学研究所, 教授 (10091464)
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| Co-Investigator(Kenkyū-buntansha) |
SHIMIZU Toshinobu Hokkaido Univ., Res.Inst.Electron. Sci., Instructor, 電子科学研究所, 助手 (60250510)
NAGAO Takashi Hokkaido Univ., Cent.Exper.Plants and Animals, Associate Professor, 実験生物センター, 助教授 (70113595)
BABA Yoshichika Hokkaido Univ., Res.Inst.Electron. Sci., Instructor, 電子科学研究所, 助手 (30238232)
TAKAHATA Masakazu Hokkaido Univ., Fac.Sci., Professor, 理学部, 教授 (10111147)
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| Project Period (FY) |
1992 – 1993
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| Keywords | Sensory neuron / Neural connection / Signal processing / Non-linearity / Wiener's analysis / Gaussian white noise / Pulse-density encoding / Hermite polynominals |
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| Research Abstract |
A method for functional dissection of sensory neuron and pulse-density encoding properties revealed by the method are studied. Spike train activities of wind sensitive mechanoreceptor neurons in cricket, in response to a Gaussian white noise stimulus, have been studied by the Wiener's analysis as a non-linear signal processing system. The 2nd order wiener kernel extracted from the system was proportional to the direct product of corresponding 1st order (linear) kernels. The system is, therefore equivalent to the cascade of a static non-linear element following the linear dynamic element whose time-domain impulse response is proportional to the 1st order kernel. The static non-linear element represents a pulse frequency modulator whose probability of spike discharge depends solely on the instantaneous amplitude of its input, i.e. memoryless. The curve of spike firing probability as a function of input amplitude was expressed by an appropriate expansion with Hermite polynominals. The input of the static non-linear element is also a Gaussian process, because of linear convolution of the Gaussian white noise of the system input. The orthogonality of the Hermite polynominals under Gaussian weight permits the determination of the coefficients of the expansion, and provides a curve of the spike firing characteristics of the neuron. The coefficients were calculated from the inner product between the observed actual outputs of the system and the polynominal values of the estimated input by linear convolution with 1st order kernel. Neurophysiological properties revealed by the present method of system identification are also described.
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