[en] Biological systems (e.g., rats) have efficient and robust localization abilities provided by the so called, place cells, which are found in the hippocampus of rodents and primates (these cells encode locations of the animal's environment). This work seeks to model these place cells by employing three (biologically plausible) techniques: Reservoir Computing (RC), Slow Feature Analysis (SFA), and Independent Component Analysis (ICA). The proposed architecture is composed of three layers, where the bottom layer is a dynamic reservoir of recurrent nodes with fixed weights. The upper layers (SFA and ICA) provides a self-organized formation of place cells, learned in an unsupervised way. Experiments show that a simulated mobile robot with 17 noisy short-range distance sensors is able to self-localize in its environment with the proposed architecture, forming a spatial representation which is dependent on the robot direction.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
ANTONELO, Eric Aislan ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Schrauwen, Benjamin
Stroobandt, Dirk
Co-auteurs externes :
yes
Langue du document :
Anglais
Titre :
Unsupervised Learning in Reservoir Computing: Modeling Hippocampal Place Cells for Small Mobile Robots
Date de publication/diffusion :
2009
Nom de la manifestation :
19th International Conference on Artificial Neural Networks
Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. The MIT Press, Cambridge (2005)
Arleo, A., Smeraldi, F., Gerstner, W.: Cognitive navigation based on nonuniform gabor space sampling, unsupervised growing networks, and reinforcement learning. IEEE Transactions on Neural Networks 15(3), 639-652 (2004)
Franzius, M., Sprekeler, H., Wiskott, L.: Slowness and sparseness lead to place, head-direction, and spatial-view cells. PLoS Comput. Biol. 3(8), 1605-1622 (2007)
Stroesslin, T., Sheynikhovich, D., Chavarriaga, R., Gerstner, W.: Robust self-localisation and navigation based on hippocampal place cells. Neural Networks 18(9), 1125-1140 (2005)
Chavarriaga, R., Strsslin, T., Sheynikhovich, D., Gerstner, W.: A computational model of parallel navigation systems in rodents. Neuroinformatics 3, 223-241 (2005)
Moser, E.I., Kropff, E., Moser, M.B.: Place cells, grid cells and the brains spatial representation system. Annual Reviews of Neuroscience 31, 69-89 (2008)
Schrauwen, B., Verstraeten, D., Van Campenhout, J.: An overview of reservoir computing: theory, applications and implementations. In: Proc. of ESANN (2007)
Jaeger, H.: The "echo state" approach to analysing and training recurrent neural networks. Technical Report GMD Report 148, German National Research Center for Information Technology (2001)
Hyvärinen, A., Oja, E.: Independent component analysis: algorithms and applications. Neural Networks 13, 411-430 (2000)
Maass, W., Natschläger, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531-2560 (2002)
Yamazaki, T., Tanaka, S.: The cerebellum as a liquid state machine. Neural Networks 20, 290-297 (2007)
Jaeger, H., Lukosevicius, M., Popovici, D.: Optimization and applications of echo state networks with leaky integrator neurons. Neural Networks 20, 335-352 (2007)
Berkes, P., Wiskott, L.: Slow feature analysis yields a rich repertoire of complex cell properties. Journal of Vision 5, 579-602 (2005)
Antonelo, E.A., Baerlvedt, A.J., Rognvaldsson, T., Figueiredo, M.: Modular neural network and classical reinforcement learning for autonomous robot navigation: Inhibiting undesirable behaviors. In: Proceedings of IJCNN, Vancouver, Canada, pp. 498-505 (2006)
Antonelo, E.A., Schrauwen, B., Stroobandt, D.: Event detection and localization for small mobile robots using reservoir computing. Neural Networks 21, 862-871 (2008)
