Information dynamics based self-adaptive reservoir for delay temporal memory tasks
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Recurrent neural networks of the reservoir computing (RC) type have been found useful in various time-series processing tasks with inherent non-linearity and requirements of variable temporal memory. Specifically for delayed response tasks involving the transient memorization of information (temporal memory), self-adaptation in RC is crucial for generalization to varying delays. In this work using information theory, we combine a generalized intrinsic plasticity rule with a local information dynamics based schema of reservoir neuron leak adaptation. This allows the RC network to be optimized in a self-adaptive manner with minimal parameter tuning. Local active information storage, measured as the degree of influence of previous activity on the next time step activity of a neuron, is used to modify its leak-rate. This results in RC network with non-uniform leak rate which depends on the time scales of the incoming input. Intrinsic plasticity (IP) is aimed at maximizing the mutual information between each neuron’s input and output while maintaining a mean level of activity (homeostasis). Experimental results on two standard benchmark tasks confirm the extended performance of this system as compared to the static RC (fixed leak and no IP) and RC with only IP. In addition, using both a simulated wheeled robot and a more complex physical hexapod robot, we demonstrate the ability of the system to achieve long temporal memory for solving a basic T-shaped maze navigation task with varying delay time scale.
KeywordsRecurrent neural networks Self-adaptation Information theory Intrinsic plasticity Temporal memory
The research leading to these results has received funding from the Emmy Noether Program DFG, MA4464/3-1, by the European Communitys Seventh Framework Programme FP7/2007-2013 (Specific Programme Cooperation, Theme3, Information and Communication Technologies) under grant agreement no.270273, Xperience, by the Federal Ministry of Education and Research(BMBF) by grants to the Bernstein Center for Computational Neuroscience (BCCN) Göttingen, grant number 01GQ1005A, project D1 and by the Max Planck Research School for Physics of Biological and Complex Systems.
- Antonelo E, Schrauwen B, Stroobandt D (2008) Mobile robot control in the road sign problem using reservoir computing networks. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp 911–916Google Scholar
- Jaeger H (2001) Short term memory in echo state networks. GMD Report 152, German National Research Center for Information TechnologyGoogle Scholar
- Jaeger H (2003) Adaptive nonlinear system identification with echo state networks. In: Advances in Neural Information Processing Systems, pp 593–600Google Scholar
- Jaeger H (2007) Discovering multiscale dynamical features with hierarchical echo state networks (Tech. Rep. No. 10). Jacobs University, BremenGoogle Scholar
- Lizier JT (2012) JIDT: an information-theoretic toolkit for studying the dynamics of complex systems. http://code.google.com/p/information-dynamics-toolkit/
- Maass W, Natschläger T, Markram H (2004) Computational models for generic cortical microcircuits. In: Computational neuroscience: a comprehensive approach, chapter 18, pp 575–605Google Scholar
- Manoonpong P, Kolodziejski C, Wörgötter F, Morimoto J (2013a) Combining correlation-based and reward-based learning in neural control for policy improvement. Adv Complex Syst (in press)Google Scholar
- Ren G, Chen W, Kolodziejski C, Wörgötter F, Dasgupta S, Manoonpong P (2012) Multiple chaotic central pattern generators for locomotion generation and leg damage compensation in a hexapod robot. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp 2756–2761Google Scholar
- Yamashita Y, Tani J (2008) Emergence of Functional hierarchy in a multiple timescale neural network model: a humanoid robot experiment. PLoS Comput Biol 4(11):e1000220. doi: 10.1371/journal.pcbi.1000220