ICONIP 2011: Neural Information Processing pp 485-492 | Cite as

A Neuro-cognitive Robot for Spatial Navigation

  • Weiwei Huang
  • Huajin Tang
  • Jiali Yu
  • Chin Hiong Tan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7062)

Abstract

This paper presents a brain-inspired neural architecture with spatial cognition and navigation capability. It captures some navigation properties of rat brain in hidden goal hunting. The brain-inspired system consists of two main parts. One part is hippocampal circuitry and the other part is hierarchical vision architecture. The hippocampus is mainly responsible for the memory and spatial navigation in the brain. The vision system provides the key information about the environment. In the experiment, the cognitive model is implemented in a mobile robot which is placed in a spatial memory task. During the navigation, the neurons in CA1 area show a place dependent response. This place-dependent pattern of CA1 guides the motor neuronal area which then dictates the robot move to the goal location. The results of current study could contribute to the development of brain-inspired cognitive map which enables the mobile robot to perform a rodent-like behavior in the navigation task.

Keywords

Hippocampus Brain-inspired model Place-dependent response Spatial memory HMAX Object recognition Neurobotics 
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References

  1. 1.
    Arel, I., Rose, D.C., Karnowski, T.P.: Deep Machine Learning–A New Frontier in Artificial Intelligence Research. IEEE Computational Intelligence Magazine 5(4), 13–18 (2010)CrossRefGoogle Scholar
  2. 2.
    Meng, Y., Zhang, Y., Jin, Y.: Autonomous Self-Reconfiguration of Modular Robots by Evolving a Hierarchical Mechanochemical Model. IEEE Computational Intelligence Magazine 6(1), 43–54 (2011)CrossRefGoogle Scholar
  3. 3.
    Dissanayake, M.W.M.G., Newman, P., Clark, S., Durrant-Whyte, H.F., Csorba, M.: A Solution to the Simultaneous Localization and Map Building (SLAM) Problem. IEEE Transactions on Robotics and Automation 17(3), 229–241 (2001)CrossRefGoogle Scholar
  4. 4.
    Bar-Shalom, Y., Fortmann, T.E.: Tracking and Data Association. Academic Press, San Diego (1988)MATHGoogle Scholar
  5. 5.
    Montemerlo, M., Thrun, S.: Simultaneous localization and mapping with unknown data association using FastSLAM. In: Proc. IEEE Int. Conference on Robotics and Automation (ICRA), pp. 1985–1991. IEEE Press, New York (2003)Google Scholar
  6. 6.
    Tang, H., Li, H., Yan, R.: Memory Dynamics in Attractor Networks with Saliency Weight. Neural Computation 22, 1899–1926 (2010)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Frank, L.M., Brown, E.N., Wilson, M.: Trajectory encoding in the hippocampus and entorhinal cortex. Neuron. 27, 169–178 (2000)CrossRefGoogle Scholar
  8. 8.
    O’Keefe, J., Nadel, L.: The Hippocampus as a Cognitive Map. Oxford University Press, Oxford (1978)Google Scholar
  9. 9.
    Barrera, A., Weitzenfeld, A.: Biological Inspired Neural Controller for Robot Learning and Mapping. In: Proc. IEEE Int. Joint Conf. on Neural Networks, pp. 3664–3671. IEEE Press, New York (2006)Google Scholar
  10. 10.
    Wyeth, B.G., Milford, M.: Spatial Cognition for Robots. IEEE Robotics & Automation Magazine 16(3), 24–32 (2009)CrossRefGoogle Scholar
  11. 11.
    Amaral, D.G., Ishizuka, N., Claiborne, B.: Neurons, numbers and the hippocampal network. Progress in Brain Research 83, 1–11 (1990)CrossRefGoogle Scholar
  12. 12.
    Bernard, C., Wheal, H.V.: Model of Local Connectivity Patterns in CA3 and CA1 Areas of The Hippocampus. Hippocampus 4, 497–529 (1994)CrossRefGoogle Scholar
  13. 13.
    Treves, A., Rolls, E.T.: Computational Analysis of The Role of The Hippocampus in Memory. Hippocampus 4, 374–391 (1994)CrossRefGoogle Scholar
  14. 14.
    Ferbinteanu, J., Shapiro, M.L.: Prospective and Retrospective Memory Coding in The Hippocampus. Neuron 40(6), 1227–1239 (2003)CrossRefGoogle Scholar
  15. 15.
    Fleischer, J.G., Krichmar, J.L.: Sensory Intergation and Remapping in a Model of the Medial Temporal Lobe during Maze Navigation by a Brain-based Device. Journal of Integrative Neuroscience 6(3), 403–431 (2007)CrossRefGoogle Scholar
  16. 16.
    Ungerleider, L.G., Haxby, J.V.: “What” and “Where” in The Human Brain. Curr. Opin. Neurobiol. 4, 157–165 (1994)CrossRefGoogle Scholar
  17. 17.
    Riesenhuber, M., Poggio, T.: Hierarchical Models of Object Recognition in Cortex. Nature Neuroscience 2(11), 1019–1025 (1999)CrossRefGoogle Scholar
  18. 18.
    Deneve, S., Latham, P.E., Pouget, A.: Reading Population Codes: A Neural Implementation of Ideal Observers. Nature Neuroscience 8(2), 740–755 (1999)Google Scholar
  19. 19.
    Krichmar, J.L., Seth, A.K., Nitz, D.A., Fleischer, J.G., Edelman, G.M.: Spatial Navigation and Causal Analysis in a Brain-Based Device Modeling Cortical-Hippocampal Interactions. Neuroinformatics 3(3), 197–221 (2005)CrossRefGoogle Scholar
  20. 20.
    Pfeifer, R., Scheier, C.: Sensory-motor coordination: The metaphor and beyond. Robot. Auton. Syst. 20, 157–178 (1997)CrossRefGoogle Scholar
  21. 21.
    Floreano, D., Mondada, F.: Evolutionary neurocontrollers for autonomous mobile robots. Neural Netw. 11, 1461–1478 (1998)CrossRefGoogle Scholar
  22. 22.
    Gaussier, P., Revel, A., Banquet, J.P., Babeau, V.: From view cells and place cells to cognitive map learning: processing stages of the hippocampal system. Biol. Cybern. 86, 15–28 (2002)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Weiwei Huang
    • 1
  • Huajin Tang
    • 1
  • Jiali Yu
    • 1
  • Chin Hiong Tan
    • 1
  1. 1.Cognitive Computing Group Institute for Infocomm ResearchAgency for Science Technology and ResearchSingapore

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