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嗅觉系统神经网络模型的模拟与动力学特性分析 被引量：1

Simulation and Analysis of an Olfactory Neural Network Model
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摘要 在哺乳动物嗅觉系统的拓扑结构及生理实验的基础上建立了一套非线性动力学神经网络模型.此模型在模拟嗅觉神经系统方面有着突出的优点,同时在信号处理以及模式识别中表现出了奇异的混沌特性.着重描述了K系列模型的非线性动力学特性,并通过数值模拟进行分析. Based on the topological structure of mammalian olfactory and physiological experiments, a set of nonlinear dynamic neural network model,K set model, has been established. This chaotic neural network can not only simulate the real brain activity of olfactory system, but also present novel chaotic concept for signal processing and pattern recognition.The nonlinear dynamic characteristics of the K set models through numeric simulation are investigated.
作者 郭宏记 李光 李绪 汪乐 沃特.费利曼
机构地区 浙江大学物理系交叉学科实验室 浙江大学生物医学工程与仪器学院 加州大学伯克力分校分子和细胞生物学系
出处 《生命科学研究》 CAS CSCD 2004年第2期140-144,共5页 Life Science Research
基金 国家973重大基础研究前期研究专项项目(2002CCAO1800)
关键词 非线性 神经网络 嗅觉 动力学 nonlinear neural network olfactory dynamic
分类号 Q811.211 [生物学—生物工程] Q429.1 [生物学—神经生物学]
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参考文献11

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二级参考文献19

  • 1[2]Hung-Jen Chang, Freeman. Parameter Optimization in Models of the Olfactory Neural System [J]. Neural Networks, 1996, 9, 1-14
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  • 3[4]Hung-Jen Chang, Freeman. Optimization of olfactory model in software to give 1/f power spectra reveals numerical instabilities in solutions governed by aperiodic (chaotic) attractors[J]. Neural Networks, 1998, 11: 449-466
  • 4[5]Freeman. A proposed name for aperiodic brain activity: stochastic chaos [J]. Neural Networks, 2000, 13:11-13
  • 5[6]Freeman, Robert Kozma. Biocomplexity: adaptive behavior in complex stochastic dynamic systems[J]. Biosystems, 2001, 59: 109-123
  • 6[7]Jose Principe, Vitor Tavares, John Harris, Freeman. Design and Implementation of a Biologically Realistic Olfactory Cortex in Analog VLSI [C]. Proceedings of the IEEE, 2001, 89( 7).
  • 7[8]Yong Yao, Freeman. Pattern recognition in olfactory systems: modeling and simulation [C]. Proceeding of the 1989 International Joint Conference on Neural Networks (IJCNN'89), IEEE Press, Piscataway, NJ, 1989, 1, 699-704.
  • 8[9]Freeman. Mass Action in the Nervous System [M]. New York: Academic,1975.
  • 9[10]Robert Kozma, Freeman. Chaotic Resonance - Methods and Applications for Robust Classification of Noisy and Variable Patterns [J]. International Journal of Bifurcation and Chaos, 2001, 11(6): 1607-1629.
  • 10[1]Hung-Jen Chang, Freeman. Local homeostasis stabilizes a model of the olfactory system globally in respect to perturbations by input during pattern classification [J]. International Journal of Bifurcation and Chaos, 1998, 8(11):2107-2123

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生命科学研究
2004年 第2期

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