Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
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.
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.
Burgess, N., Recce, M., and O’Keefe, J. (1994). A model of hippocampal function. Neural Networks 7, 1065–1081.
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.
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.
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.
Grossberg, S. (1982). Studies of Mind and Brain, Boston: Reidel Press.
Hebb, D. O. (1949). The Organization of Behavior. New York: Wiley.
Hecht-Nielsen, R. (1990). Neurocomputing, Reading, MA: Addison-Wesley Pub.
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.
Hirtle, S. C. and Jonides, J. (1985). Evidence of hierarchies in cognitive maps. Memory and Cognition 13, 208–217.
Hinton, G. and Anderson, J. A. (eds) (1981). Parallel Models of Associative Memory, Hillsdale, NJ: Lawrence Erlbaum Associates.
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.
Hopfield, J.J. and Tank, D.W. (1986). Computing with neural circuits: a model. Science 233, 625–633.
Kohonen, T. (1984). Self-organization and Associative Memory, New York: Springer-Verlag.
Kosslyn, S. and Schwartz, S.P. (1977). A simulation of visual imagery. Cognitive Science 1, 265–295.
Kuipers, B. (1978). Modeling spatial knowledge. Cognitive Science, 2,129–153.
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.
Levine, B., Jankovic, I.N. and Palij, M. (1982). Principles of spatial problem solving. Journal of Experimental Psychology: General 2, 157–175.
Lippmann, R. P. (1987). An introduction to computing with neural nets. IEEE Acoustics, Speech and Signal Processing 4, 4–22.
Lynch, K. (1960). Image Of The City, Cambridge, MA: MIT Press.
Marr, D. (1982). Vision: A Computational Investigation into the Human Representation and Processing of Visual Information, San Francisco: W.H. Freeman.
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.
Mishkin, M. and Appenzeller, T. (1987). The anatomy of memory. Scientific American 256, 80–89.
Mishkin, M., Ungerleider, L. G. and Macko, K. A. (1983). Object vision and spatial vision: two cortical pathways. Trends in Neuroscience 6, 414–417.
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.
O’Keefe, J. and Nadel, L. (1978). The Hippocampus as a Cognitive Map, New York: Oxford University Press.
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.
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.
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.
Sejnowski, T.J., Koch, C. and Churchland, P.S. (1988). Computational neuroscience. Science 241, 1299–1306.
Seibert, M. and Waxman, A. (1989). Spreading activation layers, visual saccades, and invariant representations for neural pattern recognition systems. Neural Networks 2, 9–27.
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.
Smolensky, P. (1988). On the proper treatment of connectionism. Behavioral and Brain Sciences, 11, 1–74.
Squire, L R., and Zola-Morgan, S. (1988). Memory, brain systems and behavior. TINS Special Issue II, 170–175.
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.
Steinbuch, K. (1961). Die Lernmatrix. Kybernetik 1, 36.
van der Malsburg, C. and Willshaw, D. (1981). Co-operativity and brain organization, Trends in Neurosciences 4, 80–83.
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.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1996 Kluwer Academic Publishers
About this chapter
Cite this chapter
Gopal, S. (1996). Neural Network Models of Cognitive Maps. In: Portugali, J. (eds) The Construction of Cognitive Maps. GeoJournal Library, vol 32. Springer, Dordrecht. https://doi.org/10.1007/978-0-585-33485-1_4
Download citation
DOI: https://doi.org/10.1007/978-0-585-33485-1_4
Publisher Name: Springer, Dordrecht
Print ISBN: 978-0-7923-3949-6
Online ISBN: 978-0-585-33485-1
eBook Packages: Springer Book Archive