Recurrent neural network architecture with pre-synaptic inhibition for incremental learning

Neural Networks

Volumn 19, Issue 8, 2006, Pages 1106-1119

  Ohta, Hiroyuki ^a   Gunji, Yukio Pegio ^a  

^a KOBE UNIVERSITY   (Japan)

Author keywords

Affection; Anticipation; Decision making; Incremental learning; Realization problem; Recurrent neural network

Indexed keywords

ANTICIPATION; INCREMENTAL LEARNING; PARALLEL PATHWAYS; REALIZATION PROBLEM;

DECISION MAKING; LEARNING SYSTEMS; MATHEMATICAL MODELS; PROBLEM SOLVING;

RECURRENT NEURAL NETWORKS;

ALGORITHM; ANTICIPATION; ARTICLE; CONCEPT ANALYSIS; DECISION MAKING; EXPERIMENT; EXPERIMENTAL MODEL; HUMAN; LEARNING; MEMORY; MEMORY DISORDER; NERVE CELL NETWORK; NERVE CONDUCTION; NERVE TRACT; NONHUMAN; PRESYNAPTIC INHIBITION; PRIORITY JOURNAL; REINFORCEMENT; SENSORY STIMULATION; SEQUENTIAL ANALYSIS; SIMULATION;

AFFECT; ANIMALS; FEEDBACK; HUMANS; LEARNING; NERVE NET; NEURAL INHIBITION; NEURAL NETWORKS (COMPUTER); PRESYNAPTIC TERMINALS; REINFORCEMENT (PSYCHOLOGY);

EID: 33749041444     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neunet.2006.06.005     Document Type: Article

References (19)

1. Arbib M.A., and Manes E.G. Adjoint machines, state-behavior machines, and duality. Journal of Pure and Applied Algebra 6 (1975) 313-344
2. Bergson H. Matter and memory (1899), Zone Books
3. Brooks, R. A. (1990). A robust layered control system for a mobile robot, 2-27
4. Brooks R.A. Intelligence without representation. Artificial Intelligence 47 (1991) 139-159
5. Chialvo D., and Bak P. Learning from mistakes. Neuroscience 90 (1999) 1137-1148
6. Deleuze G. Bergsonism (1991), Zone Books
7. Elman J.L. Finding structure in time. Cognitive Science 14 2 (1990) 179-211
8. Forcada, M. L. (2002). Neural networks: Automata and formal models of computation. http://www.dlsi.ua.es/mlf/nnafmc/
9. Kohonen T. Self-organizing maps (2001), Springer, New York
10. Oda Y., Kawasaki K., Morita M., Korn H., and Matsui H. Inhibitory long-term potentiation underlies auditory conditioning of goldfish escape behaviour. Nature 394 (1998) 182-185
11. Royer S., and Pare D. Conservation of total synaptic weight through balanced synaptic depression and potentiation. Nature 422 (2003) 518-522
12. Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986). Learning internal representations by error propagation, 318-362
13. Sutton R.S., and Barto A.G. Reinforcement learning (1998), MIT Press, Cambridge, MA
14. Tsukada M., and Pan X. The spatiotemporal learning rule and its efficiency in separating spatiotemporal patterns. Biological Cybernetics 92 2 (2005) 139-146
15. Williams R.J., and Zipser D. Gradient-based learning algorithms for recurrent connectionist networks. In: Chauvin Y., and Rumelhart D.E. (Eds). Backpropagation: Theory, architectures, and applications (1990), Erlbaum, Hillsdale, NJ
16. Young J.Z. A model of the brain (1964), Clarendon, Oxford
17. Young J.Z. The organization of a memory system. Proceedings of the Royal Society 163 (1965) 285-320
18. Young J.Z. The memory system of the brain (1966), University of California Press, Berkeley
19. Zottile S.J., and Faber D.S. The mauthner cell: What has it taught us. The Neuroscientist 6 (2000) 25-37