A Framework for Mobile Robot Navigation Using a Temporal Population Code

  • André Luvizotto
  • César Rennó-Costa
  • Paul Verschure
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7375)


Recently, we have proposed that the dense local and sparse long-range connectivity of the visual cortex accounts for the rapid and robust transformation of visual stimulus information into a temporal population code, or TPC. In this paper, we combine the canonical cortical computational principle of the TPC model with two other systems: an attention system and a hippocampus model. We evaluate whether the TPC encoding strategy can be efficiently used to generate a spatial representation of the environment. We benchmark our architecture using stimulus input from a real-world environment. We show that the mean correlation of the TPC representation in two different positions of the environment has a direct relationship with the distance between these locations. Furthermore, we show that this representation can lead to the formation of place cells. Our results suggest that TPC can be efficiently used in a high complexity task such as robot navigation.


Temporal Population Code Navigation Mobile Robots Place Cells Saliency Maps 


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  1. 1.
    Bonin-Font, F., Ortiz, A., Oliver, G.: Visual Navigation for Mobile Robots: A Survey. J. Intell. Robotics Syst. 53(3), 263–296 (2008)CrossRefGoogle Scholar
  2. 2.
    Jun, S., Kim, Y., Lee, J.: Difference of wavelet SIFT based mobile robot navigation. In: 2009 IEEE International Conference on Control and Automation, pp. 2305–2310. IEEE (December 2009)Google Scholar
  3. 3.
    Koch, O., Walter, M.R., Huang, A.S., Teller, S.: Ground robot navigation using uncalibrated cameras. In: 2010 IEEE International Conference on Robotics and Automation, pp. 2423–2430. IEEE (May 2010)Google Scholar
  4. 4.
    Binzegger, T., Douglas, R.J., Martin, K.A.C.: A quantitative map of the circuit of cat primary visual cortex. The Journal of Neuroscience: the Official Journal of the Society for Neuroscience 24(39), 8441–8453 (2004)CrossRefGoogle Scholar
  5. 5.
    Schubert, D., Kötter, R., Staiger, J.F.: Mapping functional connectivity in barrel-related columns reveals layer- and cell type-specific microcircuits. Brain Structure & Function 212(2), 107–119 (2007)CrossRefGoogle Scholar
  6. 6.
    Liu, B.H., Wu, G.K., Arbuckle, R., Tao, H.W., Zhang, L.I.: Defining cortical frequency tuning with recurrent excitatory circuitry. Nature Neuroscience 10(12), 1594–1600 (2007)CrossRefGoogle Scholar
  7. 7.
    Nauhaus, I., Busse, L., Carandini, M., Ringach, D.L.: Stimulus contrast modulates functional connectivity in visual cortex. Nature Neuroscience 12(1), 70–76 (2009)CrossRefGoogle Scholar
  8. 8.
    Wyss, R., Konig, P., Verschure, P.F.M.J.: Invariant representations of visual patterns in a temporal population code. Proceedings of the National Academy of Sciences of the United States of America 100(1), 324–329 (2003)CrossRefGoogle Scholar
  9. 9.
    Wyss, R., Verschure, P.F.M.J., König, P.: Properties of a temporal population code. Reviews in the Neurosciences 14(1-2), 21–33 (2003)CrossRefGoogle Scholar
  10. 10.
    Wyss, R., König, P., Verschure, P.F.M.J.: A model of the ventral visual system based on temporal stability and local memory. PLoS Biology 4(5), e120 (2006)Google Scholar
  11. 11.
    Lukoševičius, M., Jaeger, H.: Reservoir computing approaches to recurrent neural network training. Computer Science Review 3(3), 127–149 (2009)CrossRefGoogle Scholar
  12. 12.
    Samonds, J.M., Bonds, A.B.: From another angle: Differences in cortical coding between fine and coarse discrimination of orientation. Journal of Neurophysiology 91(3), 1193–1202 (2004)CrossRefGoogle Scholar
  13. 13.
    Benucci, A., Frazor, R.A., Carandini, M.: Standing waves and traveling waves distinguish two circuits in visual cortex. Neuron 55(1), 103–117 (2007)CrossRefGoogle Scholar
  14. 14.
    Gollisch, T., Meister, M.: Rapid Neural Coding in the Retina with Relative Spike Latencies. Science 319(5866), 1108–1111 (2008)CrossRefGoogle Scholar
  15. 15.
    MacEvoy, S.P., Tucker, T.R., Fitzpatrick, D.: A precise form of divisive suppression supports population coding in the primary visual cortex. Nature Neuroscience 12(5), 637–645 (2009)CrossRefGoogle Scholar
  16. 16.
    Carlsson, M.A., Knusel, P., Verschure, P.F.M.J., Hansson, B.S.: Spatio-temporal Ca2+ dynamics of moth olfactory projection neurones. European Journal of Neuroscience 22(3), 647–657 (2005)CrossRefGoogle Scholar
  17. 17.
    Knusel, P., Carlsson, M.A., Hansson, B.S., Pearce, T.C., Verschure, P.F.M.J.: Time and space are complementary encoding dimensions in the moth antennal lobe. Network 18(1), 35–62 (2007)CrossRefGoogle Scholar
  18. 18.
    Rennó-Costa, C., Luvizotto, A.L., Marcos, E., Duff, A., Sánchez-Fibla, M., Verschure, P.F.M.J.: Integrating Neuroscience-based Models Towards an Autonomous Biomimetic Synthetic. In: 2011 IEEE International Conference on RObotics and BIOmimetics (IEEE-ROBIO 2011), Phuket Island, Thailand. IEEE (2011)Google Scholar
  19. 19.
    Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)CrossRefGoogle Scholar
  20. 20.
    Luvizotto, A., Rennó-Costa, C., Pattacini, U., Verschure, P.F.M.J.: The encoding of complex visual stimuli by a canonical model of the primary visual cortex: temporal population coding for face recognition on the iCub robot. In: IEEE International Conference on Robotics and Biomimetics, Thailand, p. 6 (2011)Google Scholar
  21. 21.
    de Almeida, L., Idiart, M., Lisman, J.E.: The input-output transformation of the hippocampal granule cells: from grid cells to place fields. The Journal of Neuroscience: the Official Journal of the Society for Neuroscience 29(23), 7504–7512 (2009)CrossRefGoogle Scholar
  22. 22.
    Rennó-Costa, C., Lisman, J.E., Verschure, P.F.M.J.: The mechanism of rate remapping in the dentate gyrus. Neuron 68(6), 1051–1058 (2010)CrossRefGoogle Scholar
  23. 23.
    Mathews, Z., i Badia, S.B., Verschure, P.F.M.J.: PASAR: An integrated model of prediction, anticipation, sensation, attention and response for artificial sensorimotor systems. Information Sciences 186(1), 1–19 (2011)CrossRefGoogle Scholar
  24. 24.
    Rodieck, R.W., Stone, J.: Analysis of receptive fields of cat retinal ganglion cells. Journal of Neurophysiology 28(5), 833 (1965)Google Scholar
  25. 25.
    Einevoll, G.T., Plesser, H.E.: Extended difference-of-Gaussians model incorporating cortical feedback for relay cells in the lateral geniculate nucleus of cat. Cognitive Neurodynamics, 1–18 (November 2011)Google Scholar
  26. 26.
    Izhikevich, E.M.: Simple model of spiking neurons. IEEE Transactions on Neural Networks 14(6), 1569–1572 (2003)MathSciNetCrossRefGoogle Scholar
  27. 27.
    Izhikevich, E.M.: Which model to use for cortical spiking neurons? IEEE Transactions on Neural Networks 15(5), 1063–1070 (2004)CrossRefGoogle Scholar
  28. 28.
    Stettler, D.D., Das, A., Bennett, J., Gilbert, C.D.: Lateral Connectivity and Contextual Interactions in Macaque Primary Visual Cortex. Neuron 36(4), 739–750 (2002)CrossRefGoogle Scholar
  29. 29.
    de Almeida, L., Idiart, M., Lisman, J.E.: A second function of gamma frequency oscillations: an E%-max winner-take-all mechanism selects which cells fire. The Journal of Neuroscience: the Official Journal of the Society for Neuroscience 29(23), 7497–7503 (2009)CrossRefGoogle Scholar
  30. 30.
    Hebb, D.O.: Studies of the organization of behavior. I. Behavior of the rat in a field orientation. Journal of Comparative Psychology 25, 333–353 (1932)CrossRefGoogle Scholar
  31. 31.
    Block, M.: A note on the refraction and image formation of the rat’s eye. Vision Research 9(6), 705–711 (1969)CrossRefGoogle Scholar
  32. 32.
    D’Angelo, P.: Hugin (2010)Google Scholar
  33. 33.
    Wyss, R., Verschure, P.F.M.J.: Bounded Invariance and the Formation of Place Fields. In: Advances in Neural Information Processing Systems 16. MIT Press (2004)Google Scholar
  34. 34.
    Zeil, J., Hofmann, M.I., Chahl, J.S.: Catchment areas of panoramic snapshots in outdoor scenes. Journal of the Optical Society of America A 20(3), 450 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • André Luvizotto
    • 1
  • César Rennó-Costa
    • 1
  • Paul Verschure
    • 1
    • 2
  1. 1.The laboratory for Synthetic Perceptive, Emotive and Cognitive Systems - SPECSUniversitat Pompeu FabraBarcelonaSpain
  2. 2.ICREA - Institució Catalana de Recerca i Estudis AvançatsBarcelonaSpain

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