Neural Network Models of Cognitive Maps

  • Sucharita Gopal
Part of the GeoJournal Library book series (GEJL, volume 32)


Neural networks - parallel systems consisting of highly interconnected simple neuron-like processing elements - have become a subject of intense interest to scientists spanning a broad range of disciplines including psychology, physics, mathematics, computer science, biology and neurobiology. A variety of connectionist or neural network models have been proposed, ranging from simplified models to more realistic models of learning, associative memory, and sensori-motor development. Neural networks hold a great promise in the field of spatial cognition since they are capable (in principle) of approximating any real valued function mapping, and have been used to solve complex problems in allied fields such as visual pattern analysis and robotic control. In addition, neural networks have biological relevance, a problem that has plagued the field of symbol processing Artificial Intelligence (AI) systems. Neural networks simulated based on known physiological and anatomical properties of the brain may reveal the process by which groups of neurons interacting according to some local rales undergo self-organization. This paper will examine specific problems in spatial cognition where neural networks have been used and have produced plausible models of spatial behavior. Neural networks have helped in understanding the types of computations that might be performed by the place cells in the hippocampus when the animal moves about and constructs an internal spatial representation. Other problems in spatial cognition such as recognizing places and locating goals further demonstrate the success of neural networks in this domain. A neural network model of route learning is proposed that can learn different routes in an environment and locate a goal given the route information.


Neural Network Model Associative Memory Spatial Cognition Connectionist Model Place Field 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bachelder, I. A. and Waxman, A. (1994). Mobile robot visual mapping and localization: a view-based neurocomputational architecture that emulates hippocampal place learning. Neural Networks 7, 1083–1099.CrossRefGoogle Scholar
  2. Bachrach, J.(1991). A connectionist learning control strategy for navigation. In Neural Information Processing Systems. (R. P. Lippmann, J. E. Moody and D. S. Touretzky, eds.), pp. 457–464. San Mateo, CA: Morgan Kaufmann Publishers.Google Scholar
  3. Burgess, N., Recce, M., and O’Keefe, J. (1994). A model of hippocampal function. Neural Networks 7, 1065–1081.CrossRefGoogle Scholar
  4. Eichenbaum, H. and Buckingham, J. (1990). Studies on hippocampal processing, experiment, theory and model. In Learning and Computational Neuroscience: Foundations of Adaptive Networks (M. Gabriel and J. Moore, ed.), pp. 171–233 Cambridge, MA: Bradford book, MIT Press.Google Scholar
  5. Golledge, R.G. Smith, T.R. Pellegrino, J.W. Marshall, S.P. and Doherty, S. (1985). A conceptual model and empirical analysis of children’s acquisition of spatial knowledge. Journal of Environmental Psychology 5, 125–152.CrossRefGoogle Scholar
  6. Gopal, S., Klatzky, R., and Smith, T. (1989). NAVIGATOR: A psychologically based model of environmental learning through navigation. Journal of Environmental Psychology 9, 309–331.CrossRefGoogle Scholar
  7. Grossberg, S. (1982). Studies of Mind and Brain, Boston: Reidel Press.MATHGoogle Scholar
  8. Hebb, D. O. (1949). The Organization of Behavior. New York: Wiley.Google Scholar
  9. Hecht-Nielsen, R. (1990). Neurocomputing, Reading, MA: Addison-Wesley Pub.Google Scholar
  10. Hetherington, P. A. and Shapiro, M. L. (1993). A simple network model simulates hippocampal place fields: II. Computing goal-directed trajectories and memory fields. Behavioral Neuroscience 107, 434–443.CrossRefGoogle Scholar
  11. Hirtle, S. C. and Jonides, J. (1985). Evidence of hierarchies in cognitive maps. Memory and Cognition 13, 208–217.Google Scholar
  12. Hinton, G. and Anderson, J. A. (eds) (1981). Parallel Models of Associative Memory, Hillsdale, NJ: Lawrence Erlbaum Associates.Google Scholar
  13. Hodgkin, A. K. and Huxley, A. F. (1952). Currents carried by sodium and potassium ions through the membrane of the giant axon of Loligo. Journal of Physiology 116, 449–472.Google Scholar
  14. Hopfield, J.J. and Tank, D.W. (1986). Computing with neural circuits: a model. Science 233, 625–633.CrossRefGoogle Scholar
  15. Kohonen, T. (1984). Self-organization and Associative Memory, New York: Springer-Verlag.MATHGoogle Scholar
  16. Kosslyn, S. and Schwartz, S.P. (1977). A simulation of visual imagery. Cognitive Science 1, 265–295.CrossRefGoogle Scholar
  17. Kuipers, B. (1978). Modeling spatial knowledge. Cognitive Science, 2,129–153.CrossRefGoogle Scholar
  18. Lapedes, A. and Farber, R. (1988). How neural nets work. In Neural Information Processing Systems. (D. Z. Anderson, ed), pp. 442–443. New York: American Institute of Physics.Google Scholar
  19. Levine, B., Jankovic, I.N. and Palij, M. (1982). Principles of spatial problem solving. Journal of Experimental Psychology: General 2, 157–175.CrossRefGoogle Scholar
  20. Lippmann, R. P. (1987). An introduction to computing with neural nets. IEEE Acoustics, Speech and Signal Processing 4, 4–22.Google Scholar
  21. Lynch, K. (1960). Image Of The City, Cambridge, MA: MIT Press.Google Scholar
  22. Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information, San Francisco: W.H. Freeman.Google Scholar
  23. McNaughton, B. L.(1989). Neuronal mechanisms for spatial computation and storage, In Neural Connections, Mental Computation (L. Nadel, L.A. Cooper, P. Culicover, and R.M. Harnish, eds.), pp. 285–351. Cambridge, MA: The MIT Press.Google Scholar
  24. Mishkin, M. and Appenzeller, T. (1987). The anatomy of memory. Scientific American 256, 80–89.Google Scholar
  25. Mishkin, M., Ungerleider, L. G. and Macko, K. A. (1983). Object vision and spatial vision: two cortical pathways. Trends in Neuroscience 6, 414–417.CrossRefGoogle Scholar
  26. Muller, R. U., Kubie, J. L., and Ranck, J. B. (1987). Spatial firing patterns of hippocampus complex-spike class in a fixed environment. Journal of Neuroscience 1, 1951–1968.Google Scholar
  27. O’Keefe, J. and Nadel, L. (1978). The Hippocampus as a Cognitive Map, New York: Oxford University Press.Google Scholar
  28. O’Keefe, J. (1989). Computations that the Hippocampus might perform. In Neural Connections, Mental Computation (L. Nadel, L.A. Cooper, P. Culicover, and R.M. Harnish eds.), pp. 225–285. Cambridge, MA: The MIT Press.Google Scholar
  29. Parkinson, J. K., Murray, E. A., and Mishkin, M. (1988). A selective mnemonic role for the hippocampus in monkeys, memory for location of objects. Journal of Neuroscience 8, 4159–4167.Google Scholar
  30. Rumelhart, D.E., McClelland, J.L. and the PDP Research Group (eds.) (1986). Parallel Distributing Processing, Explorations in the Microstructure of Cognition. Volume 1. Foundations, Cambridg, M.A: The MIT Press.Google Scholar
  31. Sejnowski, T.J., Koch, C. and Churchland, P.S. (1988). Computational neuroscience. Science 241, 1299–1306.CrossRefGoogle Scholar
  32. Seibert, M. and Waxman, A. (1989). Spreading activation layers, visual saccades, and invariant representations for neural pattern recognition systems. Neural Networks 2, 9–27.CrossRefGoogle Scholar
  33. Siegel, A.W. and White, S.H. (1975). The development of spatial representations large-scale environments. In Advances In Child Development And Behavior (H.W. Reese, ed.), pp. 9–55. New York: Academic Press.Google Scholar
  34. Smolensky, P. (1988). On the proper treatment of connectionism. Behavioral and Brain Sciences, 11, 1–74.CrossRefGoogle Scholar
  35. Squire, L R., and Zola-Morgan, S. (1988). Memory, brain systems and behavior. TINS Special Issue II, 170–175.Google Scholar
  36. Squire, L R., Shimamura, A. P. and Amaral, D. G. (1989). Memory and the hippocampus. In Neural Model of Plasticity (J. H. Byrne and W. O. Berry, eds.), pp. 208–239. New York: Academic Press.Google Scholar
  37. Steinbuch, K. (1961). Die Lernmatrix. Kybernetik 1, 36.CrossRefGoogle Scholar
  38. van der Malsburg, C. and Willshaw, D. (1981). Co-operativity and brain organization, Trends in Neurosciences 4, 80–83.CrossRefGoogle Scholar
  39. Wan, H. S., Touretzky, D. S., and Redish, A. D. (1993). Towards a computational theory of rat navigation. In Proceedings of the 1993 Connectionist Models Summer School (M. Mozer, P. Smolensky, D.S. Touretzky, J.L. Elman, and A. Weigend eds.), Hillsdale, NJ: Erlbaum Associates.Google Scholar
  40. Zipser, D. (1986). Biologically plausible models of place recognition and goal location. In Parallel Distributed Processing. Explorations in the Microstructure of Cognition, Volume 2. Psychological and Biological Models (J.L. McClelland, D.E. Rumelhart, and the PDP Research Group, eds.), pp. 432–470. Cambridge, MA: The MIT Press.Google Scholar

Copyright information

© Kluwer Academic Publishers 1996

Authors and Affiliations

  • Sucharita Gopal
    • 1
  1. 1.Department of GeographyBoston UniversityBoston

Personalised recommendations