Intrinsic plasticity for reservoir learning algorithms

ESANN 2007 Proceedings - 15th European Symposium on Artificial Neural Networks

Volumn , Issue , 2007, Pages 513-518

  Wardermann, Marion ^a   Steil, Jochen ^a  

^a BIELEFELD UNIVERSITY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

BENCH-MARK PROBLEMS; ECHO STATE NETWORKS; INFORMATION TRANSMISSION; INTRINSIC PLASTICITY; LEARNING PARAMETERS; ONLINE LEARNING; RESERVOIR NETWORKS; WEIGHT MATRICES;

MATRIX ALGEBRA; NEURAL NETWORKS; SIGNAL PROCESSING;

BACKPROPAGATION;

EID: 40649114289     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. David Verstraeten, Benjamin Schrauwen, Michiel D'Haene, and Dirk Stroobandt. The unified reservoir computing concept and its digital hardware implementations. In Proceedings of the 2006 EPFL LATSIS Symposium, pages 139-140, Lausanne, 3 2006.
2. Jochen J. Steil. Backpropagation-decorrelation: Recurrent learning with o(n) complexity. In Proc. IJCNN, pages 843-848, 2004.
3. Herbert Jäger. Reservoir riddles: Suggestions for echo state network research. In Proceedings of International Joint Conference on Neural Networks, Montreal, 2005.
4. Jochen Triesch. A gradient rule for the plasticity of a neuron's intrinsic excitability. In Proc. of the Int. Conf. on Artificial Neural Networks (ICANN), 2005.
5. Wei Zhang and David J. Linden. The other side of the engram: Experience-driven changes in neuronal intrinsic excitability. Nature Reviews Neuroscience, 4:885-900, 2003.
6. Alain Destexhe and Eve Marder. Plasticity in single neuron and circuit computations. Nature, 431:789-795, 2004.
7. B. Cessac. Increase in complexity in random neural networks. Journal Physique I France, 5:409-432, 1995.
8. A. Atiya and A.G. Parlos. New results on recurrent network training: unifying the algorithmsand accelerating convergence. IEEE Trans. Neural Networks, 11(3):697-709, 2000.