Dynamic Neuronal Ensembles: Neurobiologically Inspired Discrete Event Neural Networks

  • S. Vahie
Chapter

Abstract

The past decade has seen a resurgence in the research, development and application of artificial neural networks. This is due, in part, to the adaptive and autonomous nature of these computational mechanisms that lend themselves to real-world application. Although the early motivation for the development of artificial neural networks was inspired by biological neural networks, today’s artificial neural networks have little or nothing in common with their biological counterparts. However, recent advances in neuroscience have uncovered some of the mechanisms responsible for the adaptive, communicative, and computational power of biological neural networks. Such neurobiological theories, though not always supported by mathematical formalisms, provide new insights and solutions to some of the problems facing today’s neural network formalisms. This paper highlights some of the benefits of incorporating biological mechanisms in traditional and current state-of-the-art neural networks. We employ the discrete event abstractions using the DEVS formalism to incorporate such mechanisms. Due to their dynamic topology and their analogy to biology, we call them dynamic neuronal ensembles. An application to biological defensive response demonstrates the validity of the simulation model. Potential applications to flexible control systems are discussed.

Keywords

Motor Neuron Sensory Neuron Classical Conditioning Hebbian Learning Dynamic Neuron 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    Barros, F. (1996). Dynamic Structure DSDEVS:Structural Inheritance in the DELTA Environment. AI, Simulation and Planning in High Autonomy Systems, San Diego, Web: http://www-ais.arizona.edu.Google Scholar
  2. [2]
    Braitenberg, V. Vehicles: Experiments in Synthetic Psychology. The MIT Press, Cambridge, Ma. (1987).Google Scholar
  3. [3]
    Byrne, C., V. Castellucci, and E. Kandel, Contribution of Individual Mechanoreceptor Sensory Neurons to Defensive Gill Withdrawal Reflex in Aplysia. J. Neurophysiology, 1978. 41(1): p. 413–431.Google Scholar
  4. [4]
    Caudill, Maureen and Butler, Charles. Understanding Neural Networks Volume 1: Basic Networks. The MIT Press, Massachusetts Institute of Technology, Cambridge, MA. (1994).Google Scholar
  5. [5]
    Eccles, S.J. and et al., Synapse, in The Biological Bases of Behavior, N. Chalmers, R. Crawley, and S.P.R. Rose, Editors. 1971, Open University Press: London.Google Scholar
  6. [6]
    Gautrais, J. and S. Thorpe (1998). “Rate Coding Versus Temporal Coding: A Theoretical Approach.” BioSystems 48(1–3): 57–65.CrossRefGoogle Scholar
  7. [7]
    Grossberg, S. and M. Kuperstein, Neural Dynamics of Adaptive Sensory-Motor Control. Expanded ed. ed. Neural networks, research and applications. 1989, New York: Pergamon Pub.Google Scholar
  8. [8]
    Hebb, Donald O. The Organization of Behavior: A Neurophysiological Theory, Wiley Pub. New York, NY (1949).Google Scholar
  9. [9]
    Kandel, E.R., Behavioral Biology of Aplysia. Books in Psychology, ed. R.e.a. Atkinson. 1979, San Francisco, Ca.: W. H. Freeman & Co.Google Scholar
  10. [10]
    Koestler, A. Janus: The Summing Up. Hutchinson Pub., London, UK. (1978).Google Scholar
  11. [11]
    Levitan, I.B. and L.K. Kaczmarek, The Neuron: Cellular & Molecular Biology. 1991, New York: Oxford University Press.Google Scholar
  12. [12]
    Minsky, M. and Papert, S. Perceptrons: An introduction to Computational Geometry. The MIT Press, Cambridge MA. (1969).MATHGoogle Scholar
  13. [13]
    Passino, K. and P. J. Antsaklis, Modeling &Analysis of Artificially Intelligent Planning Systems, in An Introduction to Intelligent and Automonous Control, P.J. Antsaklis and K. M. Passino, Editors. 1992, Kluwer Academic Pub.: Boston, MA. P. 191–214Google Scholar
  14. [14]
    Pollock, John L. Contemporary Theories of Knowledge. Rowman & Littlefield Pub., Inc. Savage, ML (1986).Google Scholar
  15. [15]
    Reichert, H., Introduction to Neurobiology. 1992, New York: Oxford U. Press.Google Scholar
  16. [16]
    Rosenblatt, F. Principles of neurodynamics; perceptrons and the theory of brain mechanisms, Washington, Spartan Books, 1962MATHGoogle Scholar
  17. [17]
    Rullen, R. V., J. Gautrais, et al. (1998). “Face Processing Using One Spike Per Neurone.” BioSystems 48(1–3): 229–239.CrossRefGoogle Scholar
  18. [18]
    Rumelhart, D.E. and the PDP Research group, Parallel Distributed Processing: Explorations in the microstructure of cognition, Cambridge, MA MIT Press.Google Scholar
  19. [19]
    Sankait, V. Dynamic neuronal ensembles : a new paradigm for learning, Doctoral Dissertation, Tucson, Arizona : University of Arizona, 1996Google Scholar
  20. [20]
    Shannon, CE. and J. McCarthy: Theory of Neural-Analog Reinforcement Systems & Its Applications to the Brain-Model Problem. Automata Studies, Princeton, NJ., Princeton Univ. Press.Google Scholar
  21. [21]
    Shepherd, G. and C. Koch, Introduction to Synaptic Circuits, in The Synaptic Organization of the Brain, G. Shepherd, Editor. 1990, O.U.P.: New York. p. 3–31.Google Scholar
  22. [22]
    Uhrmacher, A. M. and B. P. Zeigler (1996).Variable Structure Models in Object- Oriented Simulation. Int. J. Gen. Systems 24(4): 359–375.MATHCrossRefGoogle Scholar
  23. [23]
    Zeigler, B.P. Object-Oriented Simulation with Hierarchical Modular Models. Academic Press (1990).MATHGoogle Scholar
  24. [24]
    Zeigler, B. P., Y. Moon, et al. (1997). The DEVS Environment for High- Performance Modeling and Simulation. IEEE Comp. Sei. & Eng, 4(3): 61–71.CrossRefGoogle Scholar
  25. [25]
    Zeigler, B. P. Objects and Systems. Springer-Verlag Pub., New York, NY (1997).MATHCrossRefGoogle Scholar
  26. [26]
    Zeigler, B. P., Praehofer, H., and T.G. Kim, Theory of Modeling and Simulation, Academic Press (2000).Google Scholar

Copyright information

© Springer Science+Business Media New York 2001

Authors and Affiliations

  • S. Vahie

There are no affiliations available

Personalised recommendations