基于连通结构与动力学过程的知觉记忆层次模型
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Supported by the National Science Foundation of China under Grant Nos.60303007, 60310213 (国家自然科学基金); the National Grand Fundamental Research 973 Program of China under Grant No.2002CB312103 (国家重点基础研究发展规划(973)); the Open Foundation of the Key Laboratory of


A Hierarchical Model for Perception Memory Based on Connected Graph and Dynamic Process
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    摘要:

    根据认知的计算神经科学的观点,提出了一种基于神经系统动力学理论和连通图的信息的直接表达方式.它首先定义了知觉信息直接表达的神经结构和动力学模式,然后提出一个双层的网络计算模型,分别用于记录外界刺激的特征信息和连通对应的特定神经回路的连接模式,这是通过结构学习来实现的.在两层神经元间建立起来的连通结构同时起到联想记忆的作用,记忆的可靠程度由神经回路的连通度来决定.这种直接表达方式对于人工智能中关于语义表达和基于语义的推理研究具有重要意义.

    Abstract:

    One of the interferences between inheritance and concurrency is inheritance anomaly. From the view of cognitive computational neuroscience, a direct information representation method is presented based on neural system dynamics and graphic theory. A group of neurons and their connections representing perceptual information directly and the dynamical behaviors of neurons are defined firstly, and then a two-layer neural network is designed to record characteristics of stimulus and connect a specialized neural circuit that responding to the perception of that stimulus respectively. This could be achieved by the structure learning algorithm. The circuit constituted by neurons in two layers is also served as an associative memory of stimulus whose credibility is decided by the degree of connection of the circuit. The direct representation method is of very significance to the research of semantic representation and inference driven by semantics in artificial intelligence.

    参考文献
    [1]Thompson RF. Neurobiology of learning and memory. Science, 1986,233(4767):941~947.
    [2]Carew TJ. Molecular enhancement of memory formation. Neuron, 1996,16(1):5~8.
    [3]Kandel ER, Schwartz JH, Jessell TM. Principles of Neural Science. 4th ed., New York: McGraw-Hill Companies, Inc., 2000. 175~316.
    [4]Wilson RA, Keil FC. The MIT Encyclopedia of the Cognitive Sciences. Cambridge: MIT Press, 1999. 514~523.
    [5]Lepage M, Habib R, Cormier H, Houle S, McIntosh AR. Neural correlates of semantic associative encoding in episodic memory. Cognitive Brain Research, 2000,9(3):271~280.
    [6]Kandel ER. The molecular biology of memory storage: A dialogue between genes and synapses. Science, 2001,294(5544): 1030~1038.
    [7]Goldman-Rakic PS. Neurobiology of mental representation, In: Morowitz H, Singer J, eds. The Mind, the Brain, and CAS, SFI Studies in the Sciences of Complexity, Vol XXII. Reading: Addison-Wesley, 1995. 51~61.
    [8]Singer JL. Mental process and brain architecture: Confronting the complex adaptive systems of human thought (an overview). In: Morowitz H, Singer J, eds. The Mind, the Brain, and CAS, SFI Studies in the Sciences of Complexity, Vol XXII. Reading: Addison-Wesley, 1995. 1~29.
    [9]Sharkey NE. An oral history of neural networks. Artificial Intelligence, 2000,119(1-2):287~293.
    [10]Hattori M, Hagiwara M. Multimodule associative memory for many-to-many associations. Neurocomputing, 1998,19(1-3):99~119.
    [11]Sussner P. Observations on morphological associative memories and the kernel method. Neurocomputing, 2000,31(1-4):167~183.
    [12]Cheng ACC, Guan L. A combined evolution method for associative memory networks. Neural Networks, 1998,11(5):785~792.
    [13]Ritter GX, Diaz-de-Leon JL, Sussner P. Morphological bidirectional associative memories. Neural Networks, 1999,12(6):851~867.
    [14]Bosch H, Kurfess FJ. Information storage capacity of incompletely connected associative memories. Neural Networks, 1998,11(5): 869~876.
    [15]Ferster D, Spruston N. Cracking the neuronal code. Science, 1995,270(5237):756~757.
    [16]Sakurai Y. How do cell assemblies encode information in the brain? Neuroscience and Biobehavioral Reviews, 1999,23(6): 785~796.
    [17]Quinlan PT. Structural change and development in real and artificial neural networks. Neural Networks, 1998,11(4):577~599.
    [18]Bickhard MH, Terveen L. Foundational Issues in Artificial Intelligence and Cognitive Science. Amsterdam: Elsevier Publishing Company, 1995. 11~18.
    [19]Wermter S, Austin J, Willshaw D, Elshaw M. Towards novel neuroscience-inspired computing. In: Wermter S, Austin J, Willshaw D, eds. Emergent Neural Computational Architectures Based on Neuroscience. Berlin: Springer-Verlag, 2001. 1~19.
    [20]Tanaka K. Representation of visual features of objects in the interotemportal cortex. Neural Networks, 1996,9(8):1459~1475.
    [21]Fuster JM. Cortical dynamics of memory. Int'l Journal of Psychophysiology, 2000,35(2-3):155~164.
    [22]Sandler U, Tsitolovsky L. Fuzzy dynamics of brain activity. Fuzzy Sets and Systems, 2001,121(2):237~245.
    [23]Glassman RB. Hypothesized neural dynamics of working memory: Several chunks might be marked simultaneously by harmonic frequencies within an octave band of brain waves. Brain Research Bulletin, 1999,50(2):77~93.
    [24]Cariani P. Symbols and dynamics in the brain. BioSystems, 2001,60(1-3):59~83.
    [25]Rosser RA. Cognitive Development: Psychological and Biological Perspectives. Needham Heights: Simon & Schuster, Inc., 1994. 285~289.
    [26]Hofstadter DR. Godel, Escher, Bach--An Ethernal Braid. Beijing: Commercial Press, 1997. 456~459 (in Chinese).
    [27]Hofstadter DR.哥德尔、艾舍尔、巴赫--集异壁之大成.北京:商务印书馆,1997.456~459.
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危辉,栾尚敏.基于连通结构与动力学过程的知觉记忆层次模型.软件学报,2004,15(11):1616-1628

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  • 收稿日期:2003-05-12
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