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Self-Organizing Cortical Networks for Distributed Hypothesis Testing and Recognition Learning

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Theory and Applications of Neural Networks

Part of the book series: Perspectives in Neural Computing ((PERSPECT.NEURAL))

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Abstract

Adaptive Resonance Theory, or ART, was introduced in 1976 [1.2] in order to analyse how brain networks can learn sensory and cognitive recognition codes in a stable fashion in response to arbitrary sequences of input patterns presented under real-time conditions. ART networks are at present the only computationally realized biological theory that analyses how fast, yet stable, real-time learning of recognition codes can be accomplished in response to an arbitrary stream of input patterns. Such a general-purpose learning ability is needed by any autonomous learning agent that hopes to learn successfully about unexpected events in an unpredictable environment. One cannot restrict the agent’s processing capability if one cannot predict the environment in which it must function. Other learning theories do not have one or more essential properties that are needed for autonomous learning under real-time conditions (Table 1).

Supported in part by British Petroleum (89-A-1204). DARPA (AFOSR 90-0083), and the National Science Foundation (NSF IRI-90-00530).

Supported in part by the Air Force Office of Scientific Research (AFOSR 90-0128 and AFOSR 90-0175), the Army Research Office (ARO DAAL-03-88-K-0088), and the National Science Foundation (NSF IRI-87-16960).

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References

  1. Grossberg, S., Adaptive pattern classification and universal recoding, I: Parallel development and coding of neural feature detectors. Biological Cybernetics, 1976a; 23: 121–134.

    Article  MathSciNet  MATH  Google Scholar 

  2. Grossberg, S., Adaptive pattern classification and universal recoding, II: Feedback, expectation, olfaction, and illusions. Biological Cybernetics, 1976b; 23: 187–202.

    Article  MathSciNet  MATH  Google Scholar 

  3. Grossberg, S., Studies of mind and brain: Neural principles of learning, perception, development, cognition, and motorcontrol. Boston: Reidel Press, 1982.

    Google Scholar 

  4. Grossberg, S. (Ed.), The adaptive brain, I: cognition, learning, reinforcement, and rhythm, Amsterdam: Elsevier/North-Holland, 1987.

    Google Scholar 

  5. Grossberg, S. (Ed.), Neural networks and natural intelligence. Cambridge, MA: MIT Press, 1988.

    Google Scholar 

  6. Carpenter, G.A. and Grossberg, S., A massively parallel architecture for a self-organizing neural pattern recognition machine. Computer Vision, Graphics, and Image Processing, 1987a; 37: 54–115.

    Article  Google Scholar 

  7. Carpenter, G.A. and Grossberg, S., ART 2: Self-organization of stable category recognition codes for analog input patterns. Applied Optics, 1987b; 26: 4919–4930.

    Article  Google Scholar 

  8. Carpenter, G.A. and Grossberg, S., The ART of adaptive pattern recognition by a self-organizing neural network. Computer. Special issue on Artificial Neural Systems, 1988; 21: 77–88.

    Google Scholar 

  9. Carpenter, G.A. and Grossberg, S., ART 3: Hierarchical search using chemical transmitters in self-organizing pattern recognition architectures. Neural Networks, 1990; 3: 129–152.

    Article  Google Scholar 

  10. Grossberg, S., A neural theory of punishment and avoidance, II. Quantitative theory. Mathematical Biosciences, 1972; 15: 253–285.

    Article  MathSciNet  MATH  Google Scholar 

  11. Bear, M.F. and Singer, W., Modulation of visual cortical plasticity by acetylcholine and noradrenaline. Nature, 1986; 320: 172–176.

    Article  Google Scholar 

  12. Kasamatsu, T. and Pettigrew, J.D., Depletion of brain catecholamines: Failure of ocular dominance shift after monocular occlusion in kittens. Science, 1976; 194: 206–208.

    Article  Google Scholar 

  13. Pettigrew, J.D. and Kasamatsu, T., Local perfusion of noradrenaline maintains visual cortical plasticity. Nature, 1978; 271: 761–763.

    Article  Google Scholar 

  14. Grossberg, S., Some physiological and biochemical consequences of psychological postulates. Proceedings of the National Academy of Sciences, 1968; 60: 758–765.

    Article  MATH  Google Scholar 

  15. Grossberg, S., On the production and release of chemical transmitters and related topics in cellular control. Journal of Theoretical Biology, 1969; 22: 325–364.

    Article  Google Scholar 

  16. Grossberg, S., Classical and instrumental learning by neural networks. Progress in theoretical biology, Rosen, R. and Snell, F. (Eds.), New York: Academic Press, 3, 1974.

    Google Scholar 

  17. Kleinschmidt, A., Bear, M.F., and Singer, W., Blockade of “NMDA” receptors disrupts experience-dependent plasticity of kitten striate cortex. Science, 1987; 238: 355–358.

    Article  Google Scholar 

  18. Grossberg, S., On learning and energy-entropy dependence in recurrent and nonrecurrent signed networks. Journal of Statistical Physics, 1969b; 1: 319–350.

    Article  MathSciNet  Google Scholar 

  19. Grossberg, S., A theory of visual coding, memory, and development. Formal theories of visual perception, Leeuwenberg, E. and Buffart, H. (Eds.), New York: Wiley and Sons, 1978.

    Google Scholar 

  20. Levy, W.B., Associative changes at the synapse: LTP in the hippocampus. Synaptic modification, neuron selectivity, and nervous system organization. Levy, W.B., Anderson, J., and Lehmkuhle, S. (Eds.), Hillsdale, NJ: Erlbaum, 1985; 5–33.

    Google Scholar 

  21. Levy, W.B., Brassel, S.E., and Moore, S.D., Partial quantification of the associative synaptic learning rule of the dentate gyrus. Neuroscience, 1983; 8: 799–808.

    Article  Google Scholar 

  22. Levy, W.B. and Desmond, N.L., The rules of elemental synaptic plasticity. Synaptic modification, neuron selectivity, and nervous system organization. Levy, W.B., Anderson, J., and Lehmkuhle, S. (Eds.), Hillsdale, NJ: Erlbaum, 1985; 105–121.

    Google Scholar 

  23. Rauschecker, J.P. and Singer, W., Changes in the circuitry of the kitten’s visual cortex are gated by postsynaptic activity. Nature, 1979; 280: 58–60.

    Article  Google Scholar 

  24. Singer, W., Neuronal activity as a shaping factor in the self-organization of neuron assemblies. Synergetics of the brain. Basar, E., Flohr, H. Haken, H., and Mandell, A.J. (Eds.), New York: Springer- Verlag, 1983.

    Google Scholar 

  25. Singer, W., The role of attention in developmental plasticity. Human Neurobiology, 1982; 1: 41–43.

    Google Scholar 

  26. Eckhorn, R., Bauer, R., Jordan, W., Brosch, M., Ivruse. W., Munk, M., and Reitboeck, H.J., Coherent oscillations: A mechanism of future linking in the visual cortex? Biological Cybernetics, 1988; 60: 121– 130.

    Article  Google Scholar 

  27. Gray, C.M., Konig, P., Engel, A.K., and Singer, W., Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties. Nature, 1989; 338: 334–337.

    Article  Google Scholar 

  28. Grossberg, S., A theory of human memory: Self-organization and performance of sensory-motor codes, maps, and plans, Progress in theoretical biology, 5, Rosen, R. and Snell, F. (Eds.), New York, Academic Press, 1978, 233–374.

    Google Scholar 

  29. Näätänen, R., Gaillard, A., and Mäntysalo, S., The N1 effect of selective attention reinterpreted, Acta Psyologica, 1978; 42: 313–329.

    Article  Google Scholar 

  30. Näätänen, R., Processing negativity: An evoked potential reflection of selective attention, Psychological Bulletin, 1982; 92: 605–540.

    Article  Google Scholar 

  31. Grossberg, S., How does a brain build a cognitive code? Psychological Review, 1980; 87: 1–51.

    Article  Google Scholar 

  32. Varela, F.J. and Singer, W., Neuronal dynamics in the visual corti-cothalamic pathway revealed through binocular rivalry. Experimental Brain Research, 1987; 66: 10–20.

    Article  Google Scholar 

  33. Grossberg, S., Some psychophysiological and pharmacological correlates of a developmental, cognitive, and motivational theory. Brain and information: Event related potentials. Karrer, R., Cohen, J., and Tueting, P. (Eds.), New York: New York Academy of Sciences, 1984, 58–151.

    Google Scholar 

  34. Banquet, J.-P., Massioui, F. El, and Godet, J.L., ERP-RT chronometry and learning in normal and depressed subjects. Cerebral psychophysiology: Studies in event-related potentials, McCallum, W.C., Zappoli, R., and Denoth, F. ( Eds.) Amsterdam: Elsevier, 1986.

    Google Scholar 

  35. Banquet, J.-P. and Grossberg, S., Probing cognitive processes through the structure of event-related potentials during learning: An experimental and theoretical analysis. Applied Optics, 1987; 26: 4931–4946

    Article  Google Scholar 

  36. Kohonen, T., Self-organization and associative memory. New York: Springer-Verlag, 1984.

    MATH  Google Scholar 

  37. Grossberg, S., Neural expectation: Cerebellar and retinal analogs of cells fired by learnable or unlearned pattern classes. Kybernetik, 1972; 10: 49–57.

    Article  MATH  Google Scholar 

  38. Malsburg, C. von der, Self-organization of orientation sensitive cells in the striate cortex. Kybernetik, 1973; 14: 85–100.

    Article  Google Scholar 

  39. Willshaw, D.J. and Malsburg, C. von der, How patterned neural connections can be set up by self-organization. Proceedings of the Royal Society of London (b), 1976; 194: 431–445.

    Article  Google Scholar 

  40. Ito, M., The cerebellum and neural control. New York: Raven Press, 1984.

    Google Scholar 

  41. Kandel, E.R. and Schwartz, J.H., Principles of neural science, New York: Elsevier/North-Holland, 1981.

    Google Scholar 

  42. Kuffler, S.W., Nicholls, J.G., and Martin, A.R., From neuron to brain, 2nd edition. Sunderland, MA: Sinauer Associates, 1984.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Center for Adaptive Systems, Boston University, 111 Cummington Street, Boston, MA, 02215, USA

    Gail A. Carpenter & Stephen Grossberg

Authors
  1. Gail A. Carpenter
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  2. Stephen Grossberg
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Editor information

Editors and Affiliations

  1. Department of Mathematics, King’s College, Strand, London, WC2R 2LS, UK

    J. G. Taylor BA, BSc, MA, PhD, FInstP

  2. Department of Electrical Engineering, University of Surrey, Guildford, Surrey, GU2 5XH, UK

    C. L. T. Mannion BSc, PhD, MInstP

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© 1992 Springer-Verlag London Limited

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Carpenter, G.A., Grossberg, S. (1992). Self-Organizing Cortical Networks for Distributed Hypothesis Testing and Recognition Learning. In: Taylor, J.G., Mannion, C.L.T. (eds) Theory and Applications of Neural Networks. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-1833-6_1

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  • DOI: https://doi.org/10.1007/978-1-4471-1833-6_1

  • Publisher Name: Springer, London

  • Print ISBN: 978-3-540-19650-1

  • Online ISBN: 978-1-4471-1833-6

  • eBook Packages: Springer Book Archive

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