The Computational Model to Simulate the Progress of Perceiving Patterns in Neuron Population

  • Wen-Chuang Chou
  • Tsung-Ying Sun
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3696)


We set out here, in an effort to extend the capacities of recent neurobiological evidence and theories, to propose a computational framework, which gradually accumulates and focuses transited energy as a distribution of incitation in the cortex by means of the interaction and communication between nerve cells within different attributes. In our attempts to simulate the human neural system, we found a reproduction of the corresponding perception pattern from that which is sensed by the brain. The model successfully projects a high-dimensional signal sequence as a lower-dimensional unique pattern, while also indicating the significant active role of nerve cell bodies in the central processing of neural network, rather than a merely passive nonlinear function for input and output.


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  1. 1.
    Buck, L.B., Axel, R.: A novel multigene family may encode odorant receptors: a molecular basis for odor recognition. Cell 65, 175–187 (1991)CrossRefGoogle Scholar
  2. 2.
    Lledo, P., Gheusi, G., Vincent, J.: Information processing in the mammalian olfactory system. Physiological Reviews 85, 281–317 (2005)CrossRefGoogle Scholar
  3. 3.
    Usrey, W., Reid, R.: Synchronous activity in the visual system. Annual Review of Physiology 61, 435–456 (1999)CrossRefGoogle Scholar
  4. 4.
    Maynard, E., Hatsopoulos, N., Ojakangas, C., Acuna, B., Sanes, J., Normann, R., Donoghue, J.: Neuronal interactions improve cortical population coding of movement direction. Journal of Neuroscience 19, 8083–8093 (1999)Google Scholar
  5. 5.
    Gilbert, C.D., Sigman, M.: The neural basis of perceptual learning. Neuron 31, 681–697 (2001)CrossRefGoogle Scholar
  6. 6.
    Kohonen, T.: The self-organizing map. Proceedings of the IEEE 7, 1464–1480 (1990)CrossRefGoogle Scholar
  7. 7.
    Amari, S.-I.: Topographic organization of nerve fields. Bulletin of Mathematical Biology 42, 339–364 (1980)zbMATHMathSciNetGoogle Scholar
  8. 8.
    Haines, D.E.: Fundamental Neuroscience, 2nd edn. Churchill Livingstone, New York (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Wen-Chuang Chou
    • 1
  • Tsung-Ying Sun
    • 1
  1. 1.Department of Electrical EngineeringNational Dong Hwa UniversityHualienTaiwan

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