Neural plasticity and minimal topologies for reward-based learning

Proceedings - 8th International Conference on Hybrid Intelligent Systems, HIS 2008

Volumn , Issue , 2008, Pages 637-642

  Soltoggio, Andrea ^a  

^a UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ABS RESINS; EDUCATION; ELASTICITY; INTELLIGENT CONTROL; INTELLIGENT SYSTEMS; INTERNET; PLASTICITY; PLASTICS; PROBLEM SOLVING; TOPOLOGY;

ADAPTIVE BEHAVIOURS; ARTIFICIAL NEURAL NETWORKS; COMMON PROBLEMS; COMPLEX DYNAMICS; EMERGENT PROPERTIES; ENVIRONMENTAL FEATURES; LEARNING DYNAMICS; LEARNING PROBLEMS; LEARNING RULES; NEURAL ARCHITECTURES; NEURAL CONTROLLERS; NEURAL PLASTICITIES; ON-LINE LEARNINGS; SYNAPTIC PLASTICITIES;

NEURAL NETWORKS;

EID: 55349111506     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/HIS.2008.155     Document Type: Conference Paper

References (14)

1. W. H. Alexander and O. Sporns. An Embodied Model of Learning, Plasticity, and Reward. Adaptive Behavior, 10:143, 2002.
2. T. Back, D. B. Fogel, and Z. Michalevicz, editors. Handbook of Evolutionary Computation. Oxford University Press, Oxford, 1997.
3. P. Dayan and L. F. Abbott. Theoretical Neuroscience. MIT Press Cambridge, MA, USA, 2001.
4. J.-M. Fellous and C. Linster. Computational models of neuromodulation. Neural Computation, 10:771-805, 1998.
5. D. Floreano, P. Dürr, and C. Mattiussi. Neuroevolution: from architectures to learning. Evolutionary Intelligence, 1(1):47-62, 2008.
6. D. Floreano and J. Urzelai. Evolution of plastic control networks. Auton. Robots, 11(3):311-317, 2001.
7. W. Gerstner and M. W. Kistler. Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge, UK, August 2002.
8. S. J. Martin, P. D. Grimwood, and R. G. M. Morris. Synaptic Plasticity and Memory: An Evaluation of the Hypothesis. Annual Review of Neuroscience, 23:649-711, 2000.
9. P. R. Montague, P. Dayan, C. Person, and T. J. Sejnowski. Bee foraging in uncertain environments using predictive hebbian learning. Nature, 377:725-728, October 1995.
10. Y. Niv, D. Joel, I. Meilijson, and E. Ruppin. Evolution of Reinforcement Learning in Uncertain Environments: A Simple Explanation for Complex Foraging Behaviours. Adaptive Behaviour, 10(1):5-24, 2002.
11. E. Oja. Simplified neuron model as a principal component analyzer. Journal of Mathematical Biology, 15(3):267-273, November 1982.
12. A. Soltoggio, J. A. Bullinaria, C. Mattiussi, P. Dürr, and D. Floreano. Evolutionary Advantages of Neuromodulated Plasticity in Dynamic, Reward-based Scenarios. In Proceedings of the Artificial Life XI Conference 2008 (to appear), 2008.
13. A. Soltoggio, P. Dürr, C. Mattiussi, and D. Floreano. Evolving Neuromodulatory Topologies for Reinforcement Learning-like Problems. In Proceedings of the IEEE Congress on Evolutionary Computation, CEC 2007, 2007.
14. R. S. Sutton and A. G. Barto. Reinforcement Learning: An Introduction. MIT Press, Cambridge, MA, USA, 1998.