Skip to main content

Advertisement

SpringerLink
Log in

Using an evolutionary algorithm to determine the parameters of a biologically inspired model of head direction cells

  • Published:
Journal of Computational Neuroscience Aims and scope Submit manuscript

Abstract

A biologically inspired model of head direction cells is presented and tested on a small mobile robot. Head direction cells (discovered in the brain of rats in 1984) encode the head orientation of their host irrespective of the host’s location in the environment. The head direction system thus acts as a biological compass (though not a magnetic one) for its host. Head direction cells are influenced in different ways by idiothetic (host-centred) and allothetic (not host-centred) cues. The model presented here uses the visual, vestibular and kinesthetic inputs that are simulated by robot sensors. Real robot-sensor data has been used in order to train the model’s artificial neural network connections. The main contribution of this paper lies in the use of an evolutionary algorithm in order to determine the values of parameters that determine the behaviour of the model. More importantly, the objective function of the evolutionary strategy used takes into consideration quantitative biological observations reported in the literature.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Notes

  1. This arrangement however is not necessary. In fact, in the brain, HD cells have not been found to be arranged in any particular order that relates to their preferred head direction.

  2. http://www.mindstorms.rwth-aachen.de, last accessed on 26/5/2011.

  3. This was extracted from the results reported in Etienne et al. (1996).

  4. People with defective vestibular function.

References

  • Arleo, A., & Gerstner, W. (2001). Spatial orientation in navigating agents: Modeling head-direction cells. Neurocomputing, 38–40(1–4), 1059–1065.

    Article  Google Scholar 

  • Bailey, T., & Durrant-Whyte, H. (2006). Simultaneous localization and mapping (slam): Part II. IEEE Robotics Automation Magazine, 13(3), 108–117.

    Article  Google Scholar 

  • Bayer, H.-G., & Schwefel, H.-P. (2002). Evolution strategies. Natural Computing, 1, 3–52.

    Article  Google Scholar 

  • Benhamou, S., Bovet, P., & Poucet, B. (1995). A model for place navigation in mammals. Journal of Theoretical Biology, 173(2), 163–178.

    Article  Google Scholar 

  • Best, P. J., White, A. M., & Minai, A. (2001). Spatial processing in the brain: The activity of hippocampal place cells. Annual Review of Neuroscience, 24(1), 459–486.

    Article  PubMed  CAS  Google Scholar 

  • Biegler, R., & Morris, R. G. M. (1993). Landmark stability is a prerequisite for spatial but not discrimination learning. Nature, 361, 631–633.

    Article  PubMed  CAS  Google Scholar 

  • Blair, H. T., & Sharp, P. E. (1996). Visual and vestibular influences on head-direction cells in the anterior thalamus of the rat. Behavioral Neuroscience, 110(4), 643–660.

    Article  PubMed  CAS  Google Scholar 

  • Burgess, N., Donnett, J. G., & O’Keefe, J. (1997). Robotic and neuronal simulation of hippocampal navigation. University of Manchester, 352, 1361–6161.

    Google Scholar 

  • Cartwright, B. A., & Collett, T. S. (1983). Landmark learning in bees. Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behavioral Physiology, 151, 521–543.

    Article  Google Scholar 

  • Cuperlier, N., Quoy, M., & Gaussier, Ph. (2007). Neurobiologically inspired mobile robot navigation and planning. Frontiers in NeuroRobotics, 1(1). doi:10.3389/neuro.12.003.2007

  • de Castro, L. N., & Von Zuben, F. J. (Eds.) (2005). Recent developments in biologically inspired computing. Idea Group Publishing.

  • Degris, T., Lachèze, L., Boucheny, C., & Arleo, A. (2004) A spiking neuron model of head-direction cells for robot orientation. In In proceedings of the eighth international conference on the simulation of adaptive behavior, from animals to animats (pp. 255–263). Cambridge: MIT Press.

    Google Scholar 

  • Dissanayake, M. W. M. G., Newman, P., Clark, S., Durrant-Whyte, H. F., & Csorba, M. (2001). A solution to the simultaneous localization and map building (slam) problem. IEEE Transactions on Robotics and Automation, 17, 229–241.

    Article  Google Scholar 

  • Dorigo, M., Birattari, M., & Stützle, T. (2006). Ant colony optimization artificial ants as a computational intelligence technique. IEEE Computational Intelligence Magazine, 1, 28–39.

    Google Scholar 

  • Durrant-Whyte, H., & Bailey, T. (2006). Simultaneous localization and mapping: Part I. IEEE Robotics Automation Magazine, 13(2), 99–110.

    Article  Google Scholar 

  • Etienne, A. S., Maurer, R., & Saucy, F. (1988). Limitations in the assessment of path dependent information. Behaviour, 106, 81–111.

    Article  Google Scholar 

  • Etienne, A. S., Maurer, R., & Séguinot, V. (1996). Path integration in mammals and its interaction with visual landmarks. Journal of Experimental Biology, 199, 201–209.

    PubMed  CAS  Google Scholar 

  • Filliat, D., & Meyer, J.-A. (2003). Map-based navigation in mobile robots: I. A review of localization strategies. Cognitive Systems Research, 4(4), 243–282.

    Article  Google Scholar 

  • Goodridge, J. P., & Touretzky, D. S. (2000). Modeling attractor deformation in the rodent head-direction system. Journal of Neurophysiology, 83, 3402–3410.

    PubMed  CAS  Google Scholar 

  • Goodridge, J. P., Dudchenko, P. A., Worboys, K. A., Golob, E. J., & Taube, J. S. (1998). Cue control and head direction cells. Behavioral Neuroscience, 112(4), 749–761.

    Article  PubMed  CAS  Google Scholar 

  • Goodridge, J. P., & Taube, J. S. (1995). Preferential use of the landmark navigational system by head direction cells in rats. Behavioral Neuroscience, 109(1), 49–61.

    Article  PubMed  CAS  Google Scholar 

  • Hafting, T., Fyhn, M., Molden, S., Moser, M.-B. B., & Moser, E. I. (2005). Microstructure of a spatial map in the entorhinal cortex. Nature, 436(7052), 801–806.

    Article  PubMed  CAS  Google Scholar 

  • Kyriacou, T. (2011). An implementation of a biologically inspired model of head direction cells on a robot. In Towards autonomous robotic systems (TAROS) 2011 (to appear).

  • Levitt, T. S., & Lawton, D. T. (1990). Qualitative navigation for mobile robots. Artificial Intelligence, 44, 305–360.

    Article  Google Scholar 

  • Mataric, M. J. (1990). A distributed model for mobile robot environment-learning and navigation. Technical Report TR1228. Cambridge: Massachusetts Institute of Technology.

  • McNaughton, B. L., Chen, L. L., & Markus, E. J. (1991). “Dead reckoning,” landmark learning, and the sense of direction: A neurophysiological and computational hypothesis. J. Cognitive Neuroscience, 3, 190–202.

    Article  Google Scholar 

  • Mel, B. W., & Koch, C. (1990). Sigma-pi learning: On radial basis functions and cortical associative learning. In D. S. Touretzky (Ed.), Advances in neural information processing systems (Vol. 2, pp. 474–481). San Francisco: Morgan Kaufmann.

    Google Scholar 

  • Morris, R. G., Garrud, P., Rawlins, J. N., & O’Keefe, J. (1982). Place navigation impaired in rats with hippocampal lesions. Nature, 297(5868), 681–683.

    Article  PubMed  CAS  Google Scholar 

  • Muller, R. U., Kubie, J. L., & Ranck, J. B. (1987). Spatial firing patterns of hippocampal complex-spike cells in a fixed environment. Neuroscience, 7(7), 1935–1950.

    PubMed  CAS  Google Scholar 

  • Newman, P. M., Cole, D. M., & Ho, K. (2006). Outdoor SLAM using visual appearance and laser ranging. In Proceedings of the IEEE international conference on robotics and automation (ICRA), Orlando Florida USA.

  • O’Keefe, J., & Dostrovsky, J. (1971). The hippocampus as a spatial map. Preliminary evidence from unit activity in the freely-moving rat. Brain Research, 34(1), 171–175.

    Article  PubMed  Google Scholar 

  • Redish, A. D., Elga, A. N., & Touretzky, D. S. (1996). A coupled attractor model of the rodent head direction system. Network: Computation in Neural Systems, 7(4), 671–685.

    Article  Google Scholar 

  • Rieser, J. J., Pick, H. L., Ashmead, D. H. & Garing, A. E. (1995). Calibration of human locomotion and models of perceptual-motor organization. Journal of Experimental Psychology: Human Perception and Performance, 21(3), 480–497.

    Article  PubMed  CAS  Google Scholar 

  • Mizumori, S. J., & Williams, J. D. (1993). Directionally selective mnemonic properties of neurons in the lateral dorsal nucleus of the thalamus of rats. Neuroscience, 13(9), 4015–4028.

    PubMed  CAS  Google Scholar 

  • Solstad, T., Boccara, C. N., Kropff, E., Moser, M.-B. B., & Moser, E. I. (2008). Representation of geometric borders in the entorhinal cortex. Science (New York, N.Y.), 322(5909), 1865–1868.

    Article  CAS  Google Scholar 

  • Srinivasan, M. V., Zhang, S., Altwein, M., & Tautz, J. (2000). Honeybee navigation: Nature and calibration of the “odometer”. Science, 287(5454), 851–853.

    Article  PubMed  CAS  Google Scholar 

  • Stringer, S. M., Trappenberg, T. P., Rolls, E. T., & de Araujo, I. E. (2002). Self-organizing continuous attractor networks and path integration: One-dimensional models of head direction cells. Network: Computation in Neural Systems, 13(2), 217–242.

    CAS  Google Scholar 

  • Taube, J. S. (1998). Head direction cells and the neurophysiological basis for a sense of direction. Progress Neurobiololy, 55(3), 225–256.

    Article  CAS  Google Scholar 

  • Taube, J. S., Muller, R. U., & Ranck, Jr., J. B. (1990). Head-direction cells recorded from the postsubiculum in freely moving rats. I. Description and quantitative analysis. Neuroscience, 10(2), 420–435.

    PubMed  CAS  Google Scholar 

  • Taube, J. S., Muller, R. U., & Ranck, Jr., J. B. (1990). Head-direction cells recorded from the postsubiculum in freely moving rats. II. Effects of environmental manipulations. Neuroscience, 10(2), 436–447.

    PubMed  CAS  Google Scholar 

  • Telford, L., Howard, I. P., & Ohmi, M. (1995). Heading judgments during active and passive self-motion. Experimental Brain Research, 104, 502–510.

    Article  CAS  Google Scholar 

  • Thrun, S., Burgard, W., & Fox, D. (2005). Probabilistic robotics. Intelligent robotics and autonomous agents. Cambridge: MIT Press.

    Google Scholar 

  • Tolman, E. C. (1948). Cognitive maps in rats and men. The Psychological Review, 55(4), 189–208.

    Article  CAS  Google Scholar 

  • Tolman, E. C., Ritchie, B. F., & Kalish, D. (1946). Studies in spatial learning. I. Orientation and the short-cut. Journal of Experimental Psychology, 36, 13–24.

    Article  PubMed  CAS  Google Scholar 

  • Trullier, O., Wiener, S., Berthoz, A., & Meyer, J. (1997). Biologically-based artificial navigation systems: Review and prospects. Progress in Neurobiology, 51, 483–544.

    Article  PubMed  CAS  Google Scholar 

  • Wan, H. S., Touretzky, D. S., & Redish, A. D. (1994). Towards a computational theory of rat navigation. In Proceedings of the 1993 connectionist models summer school (pp. 11–19).

  • Wang, X.-J. (1999). Synaptic basis of cortical persistent activity: The importance of nmda receptors to working memory. The Journal of Neuroscience, 19(21), 9587–9603.

    PubMed  CAS  Google Scholar 

  • Skaggs, W. E., Knierim, J. J., Kudrimoti, H. S., & McNaughton, B. L. (1995). A model of the neural basis of the rat’s sense of direction. Advances in Neural Information Processing Systems, 7, 173–180.

    PubMed  CAS  Google Scholar 

  • Wehner, R., & Menzel, R. (1990). Do insects have cognitive maps? Annual Review of Neuroscience, 13, 403–414.

    Article  PubMed  CAS  Google Scholar 

  • Wehner, R., & Srinivasan, M. V. (1981). Searching behaviour of desert ants, genus cataglyphis (formicidae, hymenoptera). Journal of Comparative Physiology A: Neuroethology, Sensory, Neural, and Behavioral Physiology, 142, 315–338.

    Article  Google Scholar 

  • Wiener, S. I., & Taube, J. S. (Eds.) (2005). Head direction cells and the neural mechanisms of spatial orientation. Cambridge: MIT Press.

    Google Scholar 

  • Zeidman, P., & Bullinaria, J. A. (2008). Neural models of head-direction cells. In R. M. French, & E. Thomas (Eds.), From associations to rules: Connectionist models of behavior and cognition (pp. 165–177).

  • Zugaro, M. B., Arleo, A., Berthoz, A., & Wiener, S. I. (2003). Rapid spatial reorientation and head direction cells. Neuroscience, 23(8), 3478–3482.

    PubMed  CAS  Google Scholar 

Download references

Acknowledgements

The author would like to thank his colleagues Charles Day and John Butcher for the useful discussions he had with them during the work presented in this paper.

Author information

Authors and Affiliations

  1. Research Institute for the Environment, Physical Sciences and Applied Mathematics (EPSAM), Keele University, Staffordshire, UK

    Theocharis Kyriacou

Authors
  1. Theocharis Kyriacou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Theocharis Kyriacou.

Additional information

Action Editor: David Golomb

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kyriacou, T. Using an evolutionary algorithm to determine the parameters of a biologically inspired model of head direction cells. J Comput Neurosci 32, 281–295 (2012). https://doi.org/10.1007/s10827-011-0352-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10827-011-0352-x

Keywords

Search

Navigation