Map-Based Spatial Navigation: A Cortical Column Model for Action Planning

  • Louis-Emmanuel Martinet
  • Jean-Baptiste Passot
  • Benjamin Fouque
  • Jean-Arcady Meyer
  • Angelo Arleo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5248)


We modelled the cortical columnar organisation to design a neuromimetic architecture for topological spatial learning and action planning. Here, we first introduce the biological constraints and the hypotheses upon which our model was based. Then, we describe the learning architecture, and we provide a series of numerical simulation results. The system was validated on a classical spatial learning task, the Tolman & Honzik’s detour protocol, which enabled us to assess the ability of the model to build topological representations suitable for spatial planning, and to use them to perform flexible goal-directed behaviour (e.g., to predict the outcome of alternative trajectories avoiding dynamically blocked pathways). We show that the model reproduced the navigation performance of rodents in terms of goal-directed path selection. In addition, we present a series of statistical and information theoretic analyses to study the neural coding properties of the learnt space representations.


spatial navigation topological map trajectory planning cortical column hippocampal formation 


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  1. 1.
    Arleo, A., Rondi-Reig, L.: Multimodal sensory integration and concurrent navigation strategies for spatial cognition in real and artificial organisms. J. Integr. Neurosci. 6(3), 327–366 (2007)CrossRefGoogle Scholar
  2. 2.
    Dollé, L., Khamassi, M., Girard, B., Guillot, A., Chavarriaga, R.: Analyzing interactions between navigation strategies using a computational model of action selection. In: Freksa, C., et al. (eds.) SC 2008. LNCS (LNAI), vol. 5248, pp. 71–86. Springer, Heidelberg (2008)Google Scholar
  3. 3.
    O’Keefe, J., Nadel, L.: The Hippocampus as a Cognitive Map. Oxford University Press, Oxford (1978)Google Scholar
  4. 4.
    Hafting, T., Fyhn, M., Molden, S., Moser, M.B., Moser, E.I.: Microstructure of a spatial map in the entorhinal cortex. Nature 436(7052), 801–806 (2005)CrossRefGoogle Scholar
  5. 5.
    Wiener, S.I., Taube, J.S.: Head Direction Cells and the Neural Mechansims of Spatial Orientation. MIT Press, Cambridge (2005)Google Scholar
  6. 6.
    Poucet, B., Lenck-Santini, P.P., Hok, V., Save, E., Banquet, J.P., Gaussier, P., Muller, R.U.: Spatial navigation and hippocampal place cell firing: the problem of goal encoding. Rev. Neurosci. 15(2), 89–107 (2004)Google Scholar
  7. 7.
    Amaral, D.G., Witter, M.P.: The three-dimensional organization of the hippocampal formation: a review of anatomical data. Neurosci. 31(3), 571–591 (1989)CrossRefGoogle Scholar
  8. 8.
    Wilson, M.A., McNaughton, B.L.: Dynamics of the hippocampal ensemble code for space. Science 261, 1055–1058 (1993)CrossRefGoogle Scholar
  9. 9.
    Nitz, D.A.: Tracking route progression in the posterior parietal cortex. Neuron. 49(5), 747–756 (2006)CrossRefGoogle Scholar
  10. 10.
    Hok, V., Save, E., Lenck-Santini, P.P., Poucet, B.: Coding for spatial goals in the prelimbic/infralimbic area of the rat frontal cortex. Proc. Natl. Acad. Sci. USA. 102(12), 4602–4607 (2005)CrossRefGoogle Scholar
  11. 11.
    Knierim, J.J.: Neural representations of location outside the hippocampus. Learn. Mem. 13(4), 405–415 (2006)CrossRefGoogle Scholar
  12. 12.
    Granon, S., Poucet, B.: Medial prefrontal lesions in the rat and spatial navigation: evidence for impaired planning. Behav. Neurosci. 109(3), 474–484 (1995)CrossRefGoogle Scholar
  13. 13.
    Jay, T.M., Witter, M.P.: Distribution of hippocampal ca1 and subicular efferents in the prefrontal cortex of the rat studied by means of anterograde transport of phaseolus vulgaris-leucoagglutinin. J. Comp. Neurol. 313(4), 574–586 (1991)CrossRefGoogle Scholar
  14. 14.
    Kita, H., Kitai, S.T.: Amygdaloid projections to the frontal cortex and the striatum in the rat. J. Comp. Neurol. 298(1), 40–49 (1990)CrossRefGoogle Scholar
  15. 15.
    Thierry, A.M., Blanc, G., Sobel, A., Stinus, L., Golwinski, J.: Dopaminergic terminals in the rat cortex. Science 182(4111), 499–501 (1973)CrossRefGoogle Scholar
  16. 16.
    Uylings, H.B.M., Groenewegen, H.J., Kolb, B.: Do rats have a prefrontal cortex? Behav. Brain. Res. 146(1-2), 3–17 (2003)CrossRefGoogle Scholar
  17. 17.
    Aggleton, J.: The amygdala: neurobiological aspects of emotion, memory, and mental dysfunction. Wiley-Liss, New York (1992)Google Scholar
  18. 18.
    Schultz, W.: Predictive reward signal of dopamine neurons. J. Neurophysiol. 80(1), 1–27 (1998)Google Scholar
  19. 19.
    Jung, M.W., Qin, Y., McNaughton, B.L., Barnes, C.A.: Firing characteristics of deep layer neurons in prefrontal cortex in rats performing spatial working memory tasks. Cereb. Cortex 8(5), 437–450 (1998)CrossRefGoogle Scholar
  20. 20.
    Otani, S.: Prefrontal cortex function, quasi-physiological stimuli, and synaptic plasticity. J. Physiol. Paris 97(4-6), 423–430 (2003)CrossRefGoogle Scholar
  21. 21.
    Fuster, J.M.: The prefrontal cortex–an update: time is of the essence. Neuron. 30(2), 319–333 (2001)CrossRefGoogle Scholar
  22. 22.
    Cohen, J.D., Braver, T.S., Brown, J.W.: Computational perspectives on dopamine function in prefrontal cortex. Curr. Opin. Neurobiol. 12(2), 223–229 (2002)CrossRefGoogle Scholar
  23. 23.
    Mountcastle, V.B.: Modality and topographic properties of single neurons of cat’s somatic sensory cortex. J. Neurophysiol. 20(4), 408–434 (1957)Google Scholar
  24. 24.
    Mountcastle, V.B.: The columnar organization of the neocortex. Brain 120, 701–722 (1997)CrossRefGoogle Scholar
  25. 25.
    Buxhoeveden, D.P., Casanova, M.F.: The minicolumn hypothesis in neuroscience. Brain 125(5), 935–951 (2002)CrossRefGoogle Scholar
  26. 26.
    Hampson, S.: Connectionist problem solving. In: The Handbook of Brain Theory and Neural Networks, pp. 756–760. The MIT Press, Cambridge (1998)Google Scholar
  27. 27.
    Meyer, J.A., Filliat, D.: Map-based navigation in mobile robots - ii. a review of map-learning and path-planing strategies. J. Cogn. Syst. Res. 4(4), 283–317 (2003)CrossRefGoogle Scholar
  28. 28.
    Burnod, Y.: An adaptative neural network: the cerebral cortex. Masson (1989)Google Scholar
  29. 29.
    Bieszczad, A.: Neurosolver: a step toward a neuromorphic general problem solver. Proc. World. Congr. Comput. Intell. WCCI94 3, 1313–1318 (1994)Google Scholar
  30. 30.
    Frezza-Buet, H., Alexandre, F.: Modeling prefrontal functions for robot navigation. IEEE Int. Jt. Conf. Neural. Netw. 1, 252–257 (1999)Google Scholar
  31. 31.
    Hasselmo, M.E.: A model of prefrontal cortical mechanisms for goal-directed behavior. J. Cogn. Neurosci. 17(7), 1115–1129 (2005)CrossRefGoogle Scholar
  32. 32.
    Schmajuk, N.A., Thieme, A.D.: Purposive behavior and cognitive mapping: a neural network model. Biol. Cybern. 67(2), 165–174 (1992)zbMATHCrossRefGoogle Scholar
  33. 33.
    Dehaene, S., Changeux, J.P.: A hierarchical neuronal network for planning behavior. Proc. Natl. Acad. Sci. USA. 94(24), 13293–13298 (1997)CrossRefGoogle Scholar
  34. 34.
    Banquet, J.P., Gaussier, P., Quoy, M., Revel, A., Burnod, Y.: A hierarchy of associations in hippocampo-cortical systems: cognitive maps and navigation strategies. Neural Comput. 17, 1339–1384 (2005)zbMATHCrossRefGoogle Scholar
  35. 35.
    Fleuret, F., Brunet, E.: Dea: an architecture for goal planning and classification. Neural Comput 12(9), 1987–2008 (2000)CrossRefGoogle Scholar
  36. 36.
    Tolman, E.C., Honzik, C.H.: ”Insight” in rats. Univ. Calif. Publ. Psychol. 4(14), 215–232 (1930)Google Scholar
  37. 37.
    Arleo, A., Gerstner, W.: Spatial orientation in navigating agents: modeling head-direction cells. Neurocomput. 38(40), 1059–1065 (2001)CrossRefGoogle Scholar
  38. 38.
    Arleo, A., Smeraldi, F., Gerstner, W.: Cognitive navigation based on nonuniform gabor space sampling, unsupervised growing networks, and reinforcement learning. IEEE Trans. Neural. Netw. 15(3), 639–651 (2004)CrossRefGoogle Scholar
  39. 39.
    Rao, S.G., Williams, G.V., Goldman-Rakic, P.S.: Isodirectional tuning of adjacent interneurons and pyramidal cells during working memory: evidence for microcolumnar organization in pfc. J. Neurophysiol. 81(4), 1903–1916 (1999)Google Scholar
  40. 40.
    Triesch, J.: Synergies between intrinsic and synaptic plasticity mechanisms. Neural Comput. 19(4), 885–909 (2007)zbMATHCrossRefMathSciNetGoogle Scholar
  41. 41.
    Willmore, B., Tolhurst, D.J.: Characterizing the sparseness of neural codes. Netw. Comput. Neural Syst. 12(3), 255–270 (2001)CrossRefGoogle Scholar
  42. 42.
    Bialek, W., Rieke, F., de Ruyter van Steveninck, R., Warland, D.: Reading a neural code. Science 252(5014), 1854–1857 (1991)CrossRefGoogle Scholar
  43. 43.
    Samsonovich, A., Ascoli, G.: A simple neural network model of the hippocampus suggesting its pathfinding role in episodic memory retrieval. Learn. Mem. 12, 193–208 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Louis-Emmanuel Martinet
    • 1
    • 2
    • 3
  • Jean-Baptiste Passot
    • 2
    • 3
  • Benjamin Fouque
    • 1
    • 2
    • 3
  • Jean-Arcady Meyer
    • 1
  • Angelo Arleo
    • 2
    • 3
  1. 1.UPMC Univ Paris 6, FRE2507, ISIRParisFrance
  2. 2.UPMC Univ Paris 6, UMR 7102ParisFrance
  3. 3.CNRS, UMR 7102ParisFrance

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