Quantifying the effect of learning on recurrent spiking neurons

IEEE International Conference on Neural Networks - Conference Proceedings

Volumn , Issue , 2007, Pages 512-517

  Brodu, Nicolas ^a  

^a Dept of Electrical and Computer Engineering   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; COMPUTER NETWORKS; ISOMERS; MILITARY OPERATIONS; NEURONS; SEPARATION; STATISTICAL METHODS;

JOINT CONFERENCE; LEARNING RULES; SPIKING NEURONS; STATISTICAL COMPLEXITY;

NEURAL NETWORKS;

EID: 51749111852     PISSN: 10987576     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IJCNN.2007.4371009     Document Type: Conference Paper

References (14)

1. W. Maass, T. Natschläger, and H. Markram, "Real-time computing without stable states: A new framework for neural computation based on perturbations", Neural Computation, vol. 14, num. 11, pp. 2531-2560. Nov. 2002.
2. R. Legenstein and W. Maass, "What makes a dynamical system computationally powerful?" New Directions in Statistical Signal Processing: From Systems to Brain, Cambridge, MA: MIT Press, 2005.
3. S. Song, K. D. Miller, L. F. Abbott, "Competitive Hebbian learning through spike-timing-dependent synaptic plasticity", Nature Neuroscience. vol. 3, num. 9, pp 919-926, Sep. 2000.
4. D. Norton and D. Ventura, "Preparing more effective Liquid State Machines using Hebbian learning", International Joint Conference on Neural Networks, proceedings pp 8359-8364, 2006.
5. N. Brodu, "Learning using dynamical regime identification and synchronization", International Joint Conference on Neural Networks, proceedings pp 662-668, 2006.
6. C. R. Shalizi, Causal Architecture, Complexity and Self-Organization in Time Series and Cellular Automata. PhD dissertation, 2001.
7. C. R. Shalizi, "Methods and techniques of complex systems science: An overview", Chapter 1, pp 33-114, T. S. Deisboeck and J. Y. Kresh, Complex Systems Science in Biomedicine, 2006.
8. C. R. Shalizi, K. L. Shalizi, "Blind construction of optimal nonlinear recursive predictors for discrete sequences", 20th conference on Uncertainty in artificial intelligence, proc. pp 504-511, 2004.
9. C. R. Shalizi, R. Haslinger, J-B. Rouquier, K. L. Klinkner, C. Moore, "Automatic Filters for the Detection of Coherent Structure in Spatiotemporal Systems", Physical Review E, 73(3) 036104, 2005.
10. N. Brodu, "Practical investigations of complex systems", PhD dissertation, 2007, to appear.
11. E. Goodman, D. Ventura, "Spatiotemporal pattern recognition via liquid state machines", International Joint Conference on Neural Networks, proceedings pp 7579-7584, 2006.
12. Lutz Prechelt, "Proben1, a set of neural network benchmark problems and benchmarking rules". Technical Report 21/94, Fakultät für Informatik, Karlsruhe University, 1994.
13. C. R. Shalizi, M. F. Camperi, K. L. Klinkner, "Discovering Functional Communities in Dynamical Networks", to appear in proc. ICML workshop "Statistical Network Analysis: Models, Issues and New Directions", 2006.
14. W. Maass, T. Natschläger, H. Markram. "Computational models for generic cortical microcircuits". Computational Neuroscience: A Comprehensive Approach, ch 18, pp 575-605. J. Feng Ed. Chapman & Hall/CRC, 2004.