Journal of Computational Neuroscience

, Volume 4, Issue 1, pp 79–94 | Cite as

Learning Navigational Maps Through Potentiation and Modulation of Hippocampal Place Cells

  • Wulfram Gerstner
  • L.F. Abbott


We analyze a model of navigational map formation based oncorrelation-based, temporally asymmetric potentiation anddepression of synapses between hippocampal place cells. We showthat synaptic modification during random exploration of anenvironment shifts the location encoded by place cell activityin such a way that it indicates the direction from any locationto a fixed target avoiding walls and other obstacles. Multiplemaps to different targets can be simultaneously stored if weintroduce target-dependent modulation of place cell activity.Once maps to a number of target locations in a given environmenthave been stored, novel maps to previously unknown targetlocations are automatically constructed by interpolation betweenexisting maps.

maps hippocampus synaptic plasticity population coding 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abbott LF (1995) Decoding neuronal firing and modeling neural networks. Quart. Rev. Biophys. 27:291–331.Google Scholar
  2. Abbott LF, Blum KI (1996) Functional significance of long-term potentiation for sequence learning and prediction. Cerebral Cortex 6:406–416.Google Scholar
  3. Andersen RA, Essick GK, Siegel RM (1985) Encoding of spatial location by posterior parietal neurons. Science230:450–458.Google Scholar
  4. Andersen RA, Mountcastle VB (1983) The influence of the angle of gaze upon the excitability of light-sensitive neurons of the posterior parietal cortex. J. Neurosci. 3:532–548.Google Scholar
  5. Barto AG, Sutton RS, Anderson CW (1983) Neuronlike adaptive elements that can solve difficult learning problems. IEEE Trans. SMC13:835–846.Google Scholar
  6. Blum KI, Abbott LF (1996) A model of spatial map formation in the hippocampus of the rat.Neural Comp. 8:85–93Google Scholar
  7. Burgess N, Recce M, O’Keefe J (1994) A model of hippocampal function. Neural Networks7:1065–1081.Google Scholar
  8. Dayan P (1992) The convegence of TD(λ) for general λ. Machine Learning8:341–362Google Scholar
  9. Dayan P (1996) Long term potentiation, navigation and dynamic programming. Proceedings of NIPS-96(submitted).Google Scholar
  10. Debanne D, Gahwiler BH, Thompson SM (1994) Asynchronous pre-and postsynaptic activity induces associative long-term depression in area CA1 of the rat hippocampus in vitro.Proc. Natl. Acad. Sci. USA91:1148–1152.Google Scholar
  11. Eichenbaum H, Wiener SI, Shapiro ML, Cohen NJ (1988) The organization of spatial coding in the hippocampus: A study of neural ensemble activity. J. Neurosci. 9:2765–2775.Google Scholar
  12. Georgopoulos AP, Schwartz A, Kettner RE (1986) Neuronal population coding of movement direction. Science233:1416–1419.Google Scholar
  13. Gerstner W, Ritz R, van Hemmen JL (1993) Why spikes? Hebbian learning and retrieval of time-resolved excitation patterns.Biol. Cybern.69:503–515.Google Scholar
  14. Gustafsson B, Wigstrom H, Abraham WC, Huang Y-Y (1987) Long-term potentiation in the hippocampus using depolarizing current pulses as the conditioning stimulus to single volley synaptic potentials. J. Neurosci.7:774–780.Google Scholar
  15. Herz AVM, Sulzer B, Kühn R, van Hemmen JL (1989) Hebbian learning reconsidered: Representation of static and dynamic objects in associative neural nets. Biol. Cybern.60:457–467.Google Scholar
  16. Hetherington PA, Shapiro ML (1993) A simple network model simulates hippocampal place fields: II. Computing goal-directed trajectories and memory fields. Behav. Neurosci.107:434.Google Scholar
  17. Levy WB (1989) A computational approach to hippocampal function. In: RD Hawkins, GH Bower, eds. Computational Models of Learning in Simple Neural Systems. Academic Press, San Diego, CA, pp. 243–305.Google Scholar
  18. Levy WB, Steward D (1983) Temporal contiguity requirements for long-term associative potentiation/depression in the hippocampus. Neurosci.8:791–797.Google Scholar
  19. Markram H, Sakmann B (1995) Action potentials propagating back into dendrites trigger changes in efficacy of single-axon synapses between layerVpyramidal neurons. Soc. Neurosci. Abstr. 21:2007.Google Scholar
  20. McNaughton BL, Chen LL, Markus EJ (1991) Dead reckoning, landmark learning, and the sense of direction: A neurophysiological and computational hypothesis. J. Cogn. Neurosci.3:190.Google Scholar
  21. Mehta MR, Barnes CA, McNaughton BL (1996) Place field changes during route-following support a Hebbian mechanism for sequence learning (submitted).Google Scholar
  22. Minai AA, Levy WB (1993) Sequence learning in a single trial. INNS World Congress of Neural NetworksII:505–508.Google Scholar
  23. Morris RGM, Anderson E, Lynch GS, Baudry M (1986) Selective impairment of learning and blockade of long-term potentiation by an N-methyl-D-aspartate receptor antagonist, AP5. Nature319:774–776.Google Scholar
  24. Morris RGM, Schenk F, Garrud P, Rawlins JNP, O’Keefe J (1982) Place navigation impaired in rats with hippocampal lesions. Nature 297:681–683.Google Scholar
  25. Morris RGM, Anderson E, Lynch GS, Baudry M (1986) Selective impairment of learning and blockade of long-term potentiation by an N-methyl-D-aspartate receptor antagonist, AP5. Nature319:774–776.Google Scholar
  26. Muller RU, Kubie JL, Saypoff R (1991) The hippocampus as a cognitive graph. Hippocampus1:243–246.Google Scholar
  27. O’Keefe J, Burgess N (1996) Geometric determinants of the place fields of hippocampal neurons. Nature381:425–428.Google Scholar
  28. O’Keefe J, Dostrovsky J (1971) The hippocampus as a spatial map: Preliminary evidence from unit activity in the freely-moving rat. Brain Res.34:171–175.Google Scholar
  29. O’Keefe J, Nadel L (1978) The Hippocampus as a Cognitive Map Clarendon, London.Google Scholar
  30. Pouget A, Sejnowski TJ (1994) A neural model of the cortical representation of egocentric distance. Cerebral Cortex4:314–329.Google Scholar
  31. Pouget A, Sejnowski TJ (1996) Spatial transformations in the parietal cortex using basis functions. J. Cogn. Neurosci. (in press).Google Scholar
  32. Salinas E, Abbott LF (1994) Vector reconstruction from firing rates. J. Computational Neurosci.1:89–107.Google Scholar
  33. Salinas E, Abbott LF (1995) Transfer of coded information from sensory to motor networks. J. Neurosci. 15:6461–6474.Google Scholar
  34. Speakman A, O’Keefe J (1990) Hippocampal complex spike cells do no change their place fields if the goal is moved within a cue controlled environment. Eur. J. Neurosci. 2:544–555.Google Scholar
  35. Tsodyks M, Sejnowski TJ (1995) Associative memory and hippocampal place cells. Intl. J. Neural Systems6:81–86.Google Scholar
  36. Traub RD, Miles R, Muller RU, Gulyas AI (1992) Functional organization of the hippocampal CA3 regions: Implications for epilepsy, brain waves and spatial behavior. Network3:465.Google Scholar
  37. Wan HS, Touretzky DS, Redish AD (1994) Towards a computational theory of rat navigation. In: MC Mozer, P Smolensky, DS Touretzky, JL Elman, AS Weigend, eds. Proceedings of the 1993 Connectionist Models Summer School. Lawrence Erlbaum Associates, Hillsdale, NJ, pp. 11–19.Google Scholar
  38. Wilson MA, McNaughton BL (1993) Dynamics of the hippocampal ensemble code for space. Science261:1055–1058.Google Scholar
  39. Worden R (1992) Navigation by fragment fitting: A theory of hippocampal function. Hippocampus2:165.Google Scholar
  40. Zipser D, Andersen RA (1988) A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons. Nature331:679.Google Scholar

Copyright information

© Kluwer Academic Publishers 1997

Authors and Affiliations

  • Wulfram Gerstner
    • 1
  • L.F. Abbott
    • 1
  1. 1.Volen Center for Complex SystemsBrandeis UniversityWaltham

Personalised recommendations