Abstract
In this paper, we train a robot to learn online a task of obstacles avoidance. The robot has at its disposal only its visual input from a linear camera in an arena whose walls are composed of random black and white stripes. The robot is controlled by a recurrent spiking neural network (integrate and fire). The learning rule is the spike-time dependent plasticity (STDP) and its counterpart – the so-called anti-STDP. Since the task itself requires some temporal integration, the neural substrate is the network’s own dynamics. The behaviors of avoidance we obtain are homogenous and elegant. In addition, we observe the emergence of a neural selectivity to the distance after the learning process.
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References
Harvey, I., Di Paolo, E., Wood, R., Quinn, M.: Evolutionary robotics: A new scientific tool for studying cognition. Artificial Life 11(1-2), 79–98 (2005)
Soula, H., Beslon, G., Favrel, J.: Evolving spiking neurons nets to control an animat. In: Pearson, D., Steel, N., Albrecht, R. (eds.) Proc. of ICANN-GA, Roanne, France, pp. 193–197 (2003)
di Paolo, E.: Evolving spike-timing-dependent plasticity for single-trial learning in robots. Phil. Trans. R. Soc. Lond. A. 361, 2299–2319 (2003)
Floreano, D., Mattiusi, C.: Evolution of spiking neural controllers for autonomous vision-based robots. In: Gomi, T. (ed.) Evolutionnary Robotics. Springer, Heidelberg (2001)
Tuckwell, H.C.: Introduction to theoretical neurobiology: Non linear and stochastic theories, vol. 2. Cambridge University Press, Cambridge (1988)
van Vreeswijk, C., Sompolinsky, H.: Chaos in neuronal networks with balanced excitatory and inhibitory activity. Science 274, 1724–1726 (1996)
Nykamp, D.Q., Tranchina, D.: A population density approach that facilitates large-scale modeling of neural networks: Analysis and an application to orientation tuning. Journal of Computational Neuroscience 8, 19–50 (2000)
Meyer, C., van Vreeswijk, C.: Temporal correlations in stochastic networks of spiking neurons. Neural Computation 14(2), 369–404 (2002)
Chow, C.C.: Phase-locking in weakly heterogeneous neural networks. Physica D. 118, 343–370 (1998)
Gerstner, W.: Population dynamics of spiking neurons: fast transients, asynchronous states and locking. Neural Computation 12, 43–89 (2000)
Brunel, N.: Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons. Journal of Computational Neuroscience 8, 183–208 (2000)
Amit, D.J., Brunel, N.: Model of global spontaneous activity and local structured delay activity during learning periods in the cerebral cortex. Cerebral Cortex 7, 237–252 (1997)
Fusi, S., Mattia, M.: Collective behavior of networks with linear (vlsi) integrate-and-fire neurons. Neural Computation 11, 633–652 (1999)
Mattia, M., del Giudice, P.: Population dynamics of interacting spiking neurons. Physical Review E 66(5) (2002)
Soula, H., Beslon, G., Mazet, O.: Spontaneous dynamics of assymmetric random recurrent spiking neural networks. Neural Computation 18(1) (2006)
Bi, G., Poo, M.: Synaptic modifications in cultured hippocampal neurons: Dependence on spike timing, synaptic strength, and postsynaptic cell type. The Journal of Neuroscience 18(24), 10464–10472 (1998)
Abbott, L.F., Nelson, S.B.: Synaptic plasticity: taming the beast. Nature America 3, 1178–1182 (2000)
Hebb, D.O.: The Organization of Behavior. Wiley, Chichester (1949)
Rao, R.P.N., Sejnowski, T.J.: Spike-timing-dependent hebbian plasticity as temporal difference learning. Neural Computation 13, 2221–2237 (2001)
Song, S., Miller, K.D., Abbott, L.F.: Competitive hebbian learning through spike-timing dependent plasticity. Nature Neuroscience 3, 919–926 (2000)
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Soula, H., Beslon, G. (2006). Spike-Timing Dependent Plasticity Learning for Visual-Based Obstacles Avoidance. In: Nolfi, S., et al. From Animals to Animats 9. SAB 2006. Lecture Notes in Computer Science(), vol 4095. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11840541_28
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DOI: https://doi.org/10.1007/11840541_28
Publisher Name: Springer, Berlin, Heidelberg
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