A Simplified Hippocampal Model That Learns and Uses Three Kinds of Context

  • William B. Levy


Context plays a critical role in cognition. Previously Hirsh (’74), Kesner & Hardy (83), and Gray (’82) proposed that the hippocampus could learn context. Presented here is a simple hippocampal model that learns and uses three types of context: a context coming from the past, a context coming from the present, and a context concerned with the future. In all three of these situations, context is encoded by neurons that fire in a way analogous to hippocampal place cells. When these firing patterns do not appear, the network seems incapable of solving context dependent problems.

In psychology, the importance of context arises before the turn of the century (Boring, ’50), in particular Titchner advocated a critical role for context in perception. Context is important to the networks that learn language in the schemes of Pollack (’90), Elman (’90), Jordan (’86), and Mozer (’92). Although not part of the usual terminology, context is at the heart of frames and schemes used by other cognitive psychologists. More to the point here, context learning seems to be part of hippocampal function (Hirsh, ’74, Kesner & Hardy, ’83, Gray, ’82). Context learning is compatible with the Cohen and Eichenbaum theory of flexible memory (Cohen, ’84; Eichenbaum et al., ’92). Hirsh places the use of context at the center of proper encoding and recall of long-term memory. Context specifies the location of long-term memory storage. In this view, context is equivalent to episodic memory. Moreover, episodic memory associates disparate objects and events from single experiences; unfortunately, it is a lack of episodic memory that so hampers patients like H. M. and R. B. And, finally, even though not an explicit part of O’Keefe and Nadel’s (’78) cognitive mapping theory (but see Nadel and Willner, ’80), a coding that is analogous to hippocampal place cells (we call them context cells) — a coding that can be used to get from point A to point B — are context-based codes when viewed within the function of our model of the hippocampus.

To appreciate the role of context in memory, picture this one situation. You go to the hippocampal conference at Grand Cayman and for the first time you meet John Smith, a scientist from Seattle. A year or two later you visit the NIH and you see a vaguely familiar face; it’s John Smith but you cannot remember his name. (As always striving for politeness as well as wishing to avoid embarrassment, you struggle to come up with a name to match the face.) If you can only remember the place, the circumstances, the episode where you met him, then you will have a chance of remembering the name. You well up a vague association of the conference room where you met and at the same time comes the hotel, the beach, and thenchwr(133) “John, what a surprise seeing you here! How are you?”

In other words, the storage of unique events is intimately associated with the surrounding circumstances (context). Of course, the idea of context-dependent memory is a couple of thousand years old as exemplified by the Roman’s method of loci for memorizing long speeches. One sequence (the speech) is learned by associating it with another sequence of patterns (the sequence of statues you pass as you walk through a well-known museum) by using each successive statue and its locus as the context for successive words and phrases in the speech.

Less grandiose forms of context are useful in many other types of cognitive processes. Thus, many cortical regions would need to produce context codes. But, context-based codes do seem particularly important for hippocampal functions including setting up appropriately retrievable stores of memories.


Episodic Memory Hippocampal Function Grand Cayman Hippocampal Place Cell Context Code 
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. August. D. A. and Levy, W. B (1996) Temporal sequence compression by a hippocampal network model. INNS World Congress on Neural Networks, 1299–1304.Google Scholar
  2. August, D. A. and Levy, W. B (1997) Spontaneous replay of temporally compressed sequences by a hippocampal network model. CNS’96, this proceedings.Google Scholar
  3. Boring, E.G. (1950) A history of experimental psychology. 2nd edition. New York: Appleton Century Crofts.Google Scholar
  4. Cohen, N.J. (1984) Preserved learning capacity in amnesia: Evidence for multiple memory systems. In: Neuropsychology of Memory ( L.R. Squire and N. Butters. Eds.), New York: Guilford Press, pp. 83–103.Google Scholar
  5. Eichenbaum, H. and Buckingham, J. (1991) Studies on hippocampal processing: Experiment, theory. and model. In: Neurocomputation and Learning: Foundations of Adaptive Networks. ( M. Gabriel and J. Moore, Eds.), Cambridge: MIT Press. pp. 171–231.Google Scholar
  6. Eichenbaum, H., Otto, T. and Cohen. N.J. (1992) The hippocampus–what does it do’? Behay. Neural Biol., 57: 2–36.CrossRefGoogle Scholar
  7. Elman, J.L. (1990) Finding structure in time. Cog. Sci.. 14: 179–211.Google Scholar
  8. Gray, J.A. (1982) The neuropsychology of anxiety: an enquiry into the functions of the septo-hippocampal system. Oxford University Press:New York.Google Scholar
  9. Hasselmo, M.E. and Schnell, E. (1994) Laminar selectivity of the cholinergic suppression of synaptic transmission in rat hippocampal region CA I: computational modeling and brain slice physiology. J. Ncurosci., 14: 3898–3914.Google Scholar
  10. Hirsh, R. (1974) The hippocampus and contextual retrieval of information from memory. Behay. Biol., 12: 421–444.Google Scholar
  11. Jordan, M.I. (1986) Attractor dynamics and parallelism in a connectionist sequential machine. In: Proceedings of the Eighth Conference of the Cognitive Science Society. I lillsdale, NJ: Lawrence I?rlbaum Assoc. Inc., pp 531–546.Google Scholar
  12. Kesner, R.P. and Hardy, J.D. (1983) Long-term memory for contextual attributes: Dissociation of amygdala and hippocampus. Behay. Brain Res. 8, 139–149.Google Scholar
  13. Levy, W.B (1989) A computational approach to hippocampal function. In: Computational Models of Learning in Simple Neural Systems. (R.D. Hawkins and G.H. Bower. Eds.), New York: Academic Press. pp. 243–3(15.Google Scholar
  14. Levy, W. B and Wu, X. B. (1996) The relationship of local context codes to sequence length memory capacity. Network 7, 371–384.PubMedCrossRefGoogle Scholar
  15. Levy, W. B and Wu, X. B. (1997) Predicting novel paths to goals by a simple, biologically inspired neural network. CNS’96, this proceedings.Google Scholar
  16. Levy, W. B, Wu, X. B. and Baxter R. A. (1995) Unification of hippocampal function via computational/encoding considerations. In: Proceedings of the Third Workshop: Neural Networks: from Biology to High Energy Physics. International J. of Neural Sys. 6 (Supp.), 71–80.Google Scholar
  17. Levy. W.B, Wu, X. B. and Tyrcha, J.M. (1996) Solving the transverse patterning problem by learning context present: A special role for input codes. INNS World Congress on Neural Networks, 1305–1309.Google Scholar
  18. Minai, A. A., Barrows, G. L., and Levy, W. B (1994) Disambiguation of pattern sequences with recurrent networks. INNS World Congress on Neural Networks, IV-176–181.Google Scholar
  19. Mozer, M.C. (1992) Induction of muItiscaIe temporal structure. In: Advances in Neural Information Processing Systems, 4. ( J.E. Moody, S.J. Hanson and R.P. Lippmann, Eds.), San Mateo. CA: Morgan Kauffman, pp. 275–282.Google Scholar
  20. Nadel. L. and Willner,.1. (1980) Context and conditioning: A place for space. Physiol. Psychol. 8,218–228 O’Keefe. J and Nadel, L. ( 1978 ) The Hippocampus as a Cognitive Map. Oxford: Oxford Univ. Press.Google Scholar
  21. O’Reilly, R.C. and McClelland, J.L. (1994) Hippocampal conjunctive encoding, storage. and recall: avoiding a tradeoff. Hippocampus 4. 661–682.PubMedCrossRefGoogle Scholar
  22. Pollack, J.B. (1990) Recursive distributed representations. Artificial Intelligence 46: 77–105.CrossRefGoogle Scholar
  23. Wu, X. B., Baxter, R. A. and Levy, W. B (1996) Context codes and the effect of noisy learning on a simplified hippocampal CA3 model. Biol. Cybern. 74, 159–165.Google Scholar
  24. Wu, X. B. and Levy, W. B (1996) Goal finding in a simple, biologically inspired neural network. INNS World Congress on Neural Networks, 1279–1282.Google Scholar
  25. Wu, X. B., Tyrcha, J. M. and Levy, W. B (1997) A special role for input codes in solving the transverse patterning problem. CNS’96. this proceedings.Google Scholar

Copyright information

© Springer Science+Business Media New York 1997

Authors and Affiliations

  • William B. Levy
    • 1
  1. 1.Department of Neurological SurgeryUniversity of Virginia Health Sciences CenterCharlottesvilleUSA

Personalised recommendations