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Cell Assemblies as an Intermediate Level Model of Cognition

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Emergent Neural Computational Architectures Based on Neuroscience

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2036))

Abstract

This chapter discusses reverberating circuits of neurons or Cell Assemblies (CAs) derived from Hebb’s [9] proposal. It shows how CAs can quickly categorise an input and make a quick decision when pre- sented with ambiguous data. A categorisation experiment with a com- putational model of CAs shows that CAs categorise a broad range of patterns.

This chapter then describes how CAs might be used to implement the primitives of an symbolic cognitive architecture. It also shows how a system based on CAs is theoretically capable of fast learning, variable binding, rule application, integration with emotion and integration with the external environment.

CAs are thus an ideal mechanism for further research into both compu- tational and cognitive neural models. Our medium to long-term plan for exploration of thought via CAs is described.

If humans use CAs as a basis of thought, then studying how biological systems use CAs will provide information for computational models. The reverse is also true; computational modelling can direct our research activity in biological neural systems.

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References

  1. Abeles, M., H. Bergman, E. Margalit, and E. Vaadia. (1993) Spatiotemporal Firing Patterns in the Frontal Cortex of Behaving Monkeys. Journal of Neurophysiology 70(4):1629–38

    Google Scholar 

  2. Anderson, John R. (1983). The Architecture of Cognition Cambridge, MA: Harvard University Press.

    Google Scholar 

  3. Bechtel, W. and A. Abrhamsen. (1991). Connectionism and the Mind Blackwell Publishers.

    Google Scholar 

  4. Bower, J. and D. Beeman (1994). The Book of Genesis Springer-Verlag.

    Google Scholar 

  5. Bratenberg, V. (1989). Some Arguments for a Theory of Cell Assemblies in the Cerebral Cortex In Neural Connections, Mental Computation Nadel, Cooper, Culicover and Harnish eds. MIT Press.

    Google Scholar 

  6. Fransen, E., A. Lanser, and H. Liljenstrom. (1992) A Model of Cortical Associative Memory Based on Hebbian Cell Assemblies. In Connectionism in a Broad Perspective. Niklasson, L. and M. Boden eds. Ellis Horwood Springer-Verlag pp. 165–71

    Google Scholar 

  7. Fujii, Hiroshi, I. Hiroyuke, K. Aihara, N. Ichinose and M. Tsukada. 1996. Dynamical Cell Assembly Hypothesis-Theoretical Possibility of Spatio-temporal Coding in the Cortex. In Neural Networks Vol 9:8 pp. 1303–50

    Google Scholar 

  8. Hayes-Roth, B. (1985) A Blackboard Architecture for Control. In Artificial Intelligence 26 pp. 251–321.

    Article  Google Scholar 

  9. Hebb, D.O. (1949) The Organization of Behavior. John Wiley and Sons, New York.

    Google Scholar 

  10. Hetherington, P. A., and M. Shapiro. (1993) Simulating Hebb cell assemblies: the necessity for partitioned dendritic trees and a post-net-pre LTD rule. Network: Computation in Neural Systems 4:135–153

    Article  Google Scholar 

  11. Huyck, Christian R. (2000). Modelling Cell Assemblies. In Proceedings of the International Conference of Artificial Intelligence Las Vegas, Nevada

    Google Scholar 

  12. Ivancich, J.E., C. Huyck and S. Kaplan. 1999. Cell Assemblies as Building Blocks of Larger Cognitive Structures. In Behaviour and Brain Science. Cambridge University Press

    Google Scholar 

  13. Kaplan, S., M. Weaver, and R. French (1990) Active Symbols and Internal Models: Towards a Cognitive Connectionism. In AI and Society 4:51–71

    Article  Google Scholar 

  14. Kaplan, S., M. Sontag, and E. Chown. (1991) Tracing recurrent activity in cognitive elements(TRACE): A model of temporal dynamics in a cell assembly. Connection Science 3:179–206

    Article  Google Scholar 

  15. Katz, P.S. (1999) What are we talking about? Modes of neuronal communication. In Beyond Neurotransmission: Neuromodulation and its Importance for Information Processing Paul Katz ed. Oxford Press. pp. 3–30

    Google Scholar 

  16. Laird, John E., A. Newell, and P. Rosenbloom. (1987) Soar: An Architecture for General Cognition. In Artificial Intelligence 33,1

    Google Scholar 

  17. Lippmann, R.P. (1987) An Introduction to Computing with Neural Nets. IEEE ASSP Magazine April 1987.

    Google Scholar 

  18. Malsberg, C. von der. (1986) Am I Thinking Assemblies? In Brain Theory. Palm, G. and A. Aersten eds. Springer-Verlag pp. 161–76

    Google Scholar 

  19. Newell, Allen. 1990. Unified Theories of Cognition Cambridge, MA: Harvard University Press.

    Google Scholar 

  20. Palm, G. (2000) Robust identification of visual shapes enhanced by synchronisation of cortical activity. In EmerNet: Third International Workshop on Current Computational Architectures Integrating Neural Networks and Neuroscience. Wermter, S. ed.

    Google Scholar 

  21. Pulvermuller, F. (1999) Words in the brain’s language. In Behavioral and Brain Sciences 22 pp. 253–336.

    Article  Google Scholar 

  22. Reddy, R., L. Erman, R. Fennell, and R. Neely. (1976) The HEARSAY speech understanding system: An example of the recognition process. In EIII Transactions on Computers C-25:427–431

    Google Scholar 

  23. Rosch, Eleanor and C. Mervis. 1975. Family Resemblances: Studies in the Interna Structure of Categories. In Cognitive Psychology 7 pp. 573–605.

    Google Scholar 

  24. Schuz, A. (1995) Neuroanatomy in a Computational Perspective. In The Handbook of Brain Theory and Neural Networks. Arbib, M. ed. MIT Press pp. 622–626.

    Google Scholar 

  25. Shastri, L. (2000) SMRITI: a computational model of episodic memory formation inspired by the hippocampal system. In EmerNet: Third International Workshop on Current Computational Architectures Integrating Neural Networks and Neuroscience. Wermter, S. ed.

    Google Scholar 

  26. Spatz, H. (1996) Hebb’s concept of synaptic plasticity of neuronal cell assemblies. In Behavioural Brain Research 78 pp. 3–7.

    Article  Google Scholar 

  27. Sun, R. (1995) Robust reasoning; integrating rule-based and similarity-based reasoning. In Artificial Intelligence 75 pp. 241–95.

    Article  Google Scholar 

  28. Wickelgren, W.A. (1999) Webs, Cell Assemblies, and Chunking in Neural Nets. Canadian Journal of Experimental Psychology 53:1 pp. 118–131

    Google Scholar 

  29. Wittgenstein L. (1953) Philosophical Investigations Blackwell, Oxford.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Middlesex University, London, UK

    Christian R. Huyck

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  1. Christian R. Huyck
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Editors and Affiliations

  1. Centre of Informatics, SCET, University of Sunderland, St PetersWay, Sunderland SR6 0DD, UK

    Stefan Wermter

  2. Department of Computer Science, University of York, York, Y010 5DD, UK

    Jim Austin

  3. Institute for Adaptive and Neural Computation, University of Edinburgh, 5 Forrest Hill, Edinburgh, UK

    David Willshaw

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Huyck, C.R. (2001). Cell Assemblies as an Intermediate Level Model of Cognition. In: Wermter, S., Austin, J., Willshaw, D. (eds) Emergent Neural Computational Architectures Based on Neuroscience. Lecture Notes in Computer Science(), vol 2036. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44597-8_28

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  • DOI: https://doi.org/10.1007/3-540-44597-8_28

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-42363-8

  • Online ISBN: 978-3-540-44597-5

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