位相縮約に基づくシリコンニューロンのダイナミカルシステムデザイン  [in Japanese] Dynamical Systems Design of Silicon Neurons based on Phase Reduction Theory  [in Japanese]

Search this Article

Author(s)

    • 三浦 佳二 MIURA Keiji
    • 東北大学大学院情報科学研究科 Collaborative Mathematics Research Unit,Graduate School of Information Sciences,Tohoku University
    • 浅井 哲也 ASAI Tetsuya
    • 北海道大学大学院情報科学研究科 Graduate School of Information Science and Technology,Hokkaido University

Abstract

近年,非線形システムの持つ数理構造に基づくダイナミカルシステムデザインアプローチの研究が進展している.ダイナミカルシステムデザインでは,設計対象をダイナミカルシステムとして捉え,その数理構造を解析し,所望の動作特性を再現するように,設計対象の持つ定性的特性を物理プラットフォーム上に実装する.ダイナミカルシステムデザインは,設計において着目するシステムの数理構造により,相空間軌道ベース,ポテンシャルベース,および位相応答関数ベースの手法に分けられる.本研究では,位相縮約理論の観点から,シリコンニューロンの位相応答特性に着目した設計手法について提案する.まず,先行研究におけるシリコンニューロンの位相応答特性を解析することで,設計基準を明確にする.次に,具体的な設計対象として,共振-発火型ニューロン(Resonate-and-Fire Neuron;RFN)回路の設計を行う.さらに,RFN回路を要素回路とした相互結合ネットワークを構成し,回路単体の位相応答特性が伝達遅延のあるネットワークの位相同期特性を向上させることを示す.

Recently, dynamical systems design approaches based the mathematical structure of nonlinear systems have been developed. In the dynamical systems design, the mathematical structure of target devices and circuits are analyzed as a dynamical system, and the qualitative nature of the devices and circuits are implemented to reproduce desirable dynamical behaviors on a practical physical platform. In terms of the mathematical structures embedded in the devices and circuits, the dynamical systems approaches can be classified as: the phase plane and nullcline-based design, the potential based design, and the phase response curve-based design. In this report, we apply the third approach to designing silicon neurons (SiNs) from the viewpoint of the phase reduction theory. Firstly we clarify design criteria by analyzing the phase response properties of various SiNs presented in previous works. Secondly, we design the resonate-and-fire neuron (RFN) circuit as a specific target SiN according to the criteria. Finally, we show that the synchronization properties of a fully-connected network of the RFN circuits with transmission delays can be improved by tuning the phase response properties of the element circuit.

Journal

  • IEICE technical report. Neurocomputing

    IEICE technical report. Neurocomputing 112(227), 139-144, 2012-09-27

    The Institute of Electronics, Information and Communication Engineers

References:  23

  • <no title>

    MEAD C.

    Analog vlsi and neural systems, 1989

    Cited by (1)

  • <no title>

    LIU S.-C.

    Analog VLSI: Circuits and principleS, 2002

    Cited by (1)

  • <no title>

    LIU S.-C.

    Neuromorphic sensory systems current opinion in neurobiology 20(3), 288-295, 2010

    Cited by (1)

  • A silicon ueurori

    MAHOWALD M.

    Nature 354, 515-518, 1991

    Cited by (1)

  • <no title>

    NAKADA K.

    IEEE Trans, on Circuits and systems i 52(6), 1095-1103, 2005

    Cited by (1)

  • Neuromorphic silicon neuron circuits

    INDIVERI G.

    Frontiers in neuroseience 5, 2011

    Cited by (1)

  • <no title>

    NAKADA K.

    iNTERNATIONAL JOURNAL OF NEURAL SYSTEMS 16(6), 445-456, 2006

    Cited by (1)

  • <no title>

    MIZOGUCHI N.

    CIAL LIFE AND ROBOTICS 16(3), 383-388, 2011

    Cited by (1)

  • <no title>

    SIMONI M.

    ieee tRANS bIOMEDICAL ENGINEERING 51(2), 342-354, 2004

    Cited by (1)

  • <no title>

    CYMBALYUK G.

    Neural Computation^. 12, 2259-2278, 2000

    Cited by (1)

  • <no title>

    FARQUHAR E.

    IEEE Tran.s Circuits and Systems 52(3), 477-488, 2005

    Cited by (1)

  • <no title>

    KOLUIO T.

    IEEE Trans. Neural Networks 16(3), 754-773, 2005

    Cited by (1)

  • <no title>

    KOHNO T.

    eurocomputing 71(7-9), 1619-1628, 2008

    Cited by (1)

  • <no title>

    BASU A.

    IEEE T rans Circuits and systems i 57(11), 2938-2947, 2010

    Cited by (1)

  • <no title>

    ARTHUR J.

    IEEE Trans. Circuits and Systems 58(5), 1034-1043, 2010

    Cited by (1)

  • <no title>

    ADAMATZKY A.

    Reaction-diffusion computers, 2005

    Cited by (1)

  • <no title>

    SUENAGA S.

    IEICE Trans fundarnentats E90, 715-723, 2007

    Cited by (1)

  • <no title>

    UTAGAWA A.

    IEICE NOLTA, 2(4), 409-416, 2011

    Cited by (1)

  • <no title>

    NAKADA K.

    presented at IEICE CCS Technical works hop TOKYO JAPAN March, 2012

    Cited by (1)

  • <no title>

    NAKADA K.

    IEICE Technical Report 111, 89-94, 2012

    Cited by (1)

  • <no title>

    IZHIKEVICH E. M.

    Dynamical systems in neuroscience: the geometry of excitability and bursting, 2007

    Cited by (1)

  • <no title>

    KURAMOTO Y.

    Chemical oscillations, waves, and turbulence, 1984

    Cited by (1)

  • <no title>

    TSUBO Y.

    The European Journal of Neuroscience 25, 3429-3441, 2007

    Cited by (1)

Codes

  • NII Article ID (NAID)
    110009636977
  • NII NACSIS-CAT ID (NCID)
    AN10091178
  • Text Lang
    JPN
  • Article Type
    ART
  • ISSN
    0913-5685
  • NDL Article ID
    024076803
  • NDL Call No.
    Z16-940
  • Data Source
    CJP  NDL  NII-ELS 
Page Top