Publications of Legenstein, R.

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16 publication entries, 0 of them (printed in bold in the list) acknowledge the project support.
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Book chapter
Conference contribution: poster

Paper (reviewed)

Buesing et al. 2009	Buesing, L., Schrauwen, B., and Legenstein, R.	Connectivity, Dynamics, and Memory in Reservoir Computing with Binary and Analog Neurons	Neural Computation (2010) 22(5):1272-1311	abstract, fulltext
Klampfl et al. 2007b	Klampfl, S., Legenstein, R. and Maass, W.	Spiking neurons can learn to solve information bottleneck problems and to extract independent components	Neural Comput. (2009) 21(4):911-59. doi:10.1162/neco.2008.01-07-432	abstract
Legenstein and Maass 2006	Legenstein, R.A. and Maass, W.	A criterion for the convergence of learning with spike timing dependent plasticity	Advances in Neural Information Processing System (MIT Press). (2006) 18:763-770	abstract, fulltext
Legenstein and Maass 2007	Legenstein, R. and Maass, W.	On the classification capability of sign-constrained perceptrons	Neural Comput. (2008) 20(1):288-309	abstract, fulltext
Legenstein and Maass 2007c	Legenstein, R.A. and Maass, W.	Edge of chaos and prediction of computational performance for neural microcircuit models	Neural Networks (2007) 20(3): 323-334	abstract, fulltext
Legenstein et al. 2008	Legenstein, R., Pecevski, D. and Maass, W.	A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback	PLoS Computational Biology (2008) 4(10): e1000180	abstract, fulltext
Schrauwen et al. 2008	Schrauwen, B., Büsing, L. and Legenstein, R.	On computational power and the order-chaos phase transition in reservoir computing	In Proc. of NIPS 2008: Advances in Neural Information Processing Systems, volume 21. MIT Press, 2009	fulltext

Book chapter

Legenstein and Maass 2006b	Legenstein, R. and Maass, W.	What makes a dynamical system computationally powerful?	In S. Haykin, J. C. Principe, T.J. Sejnowski, and J.G. McWhirter, editors, New Directions in Statistical Signal Processing: From Systems to Brain. pages 127-154. MIT Press, 2007	abstract, fulltext
Legenstein and Maass 2007b	Legenstein, R. and Maass, W.	What makes a dynamical system computationally powerful?	In S. Haykin, J. C. Principe, T. Sejnowski, and J. McWhirter, editors, New Directions in Statistical Signal Processing: From Systems to Brain, pages 127-154. MIT Press, 2007	abstract, fulltext

Conference contribution: poster

	Legenstein, R.	A criterion for the convergence of learning with spike timing dependent plasticity	Vancouver, CA, Conf. Neural Information Processing Systems, NIPS*05, December 2005
	Legenstein, R.	Analysis of Cortical Microcircuits on the Systems Level	Salt Lake City, USA, Cosyne 2006, March 2006
	Legenstein, R.	Spiking Neural Networks	Porto, Portugal, NN 2006; Neural Networks in Classification Regression and Data
Klampfl et al. 2007	Klampfl, S., Legenstein, R.A. and Maass, W.	Information bottleneck optimization and independent component extraction with spiking neurons	Proc. of NIPS 2006, Advances in Neural Information Processing Systems, volume 19. MIT Press, 2007	abstract, fulltext
Legenstein et al. 2008b	Legenstein, R., Pekevski, D. and Maass, W.	Theoretical analysis of learning with reward-modulated spike-timing-dependent plasticity	Proc. of NIPS 2007, Advances in Neural Information Processing Systems, volume 20. MIT Press, 2008	abstract
Legenstein et al. 2009	Legenstein, R., Maass, W., Chase, S.M. and Schwartz, A.B.	Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning	Proc. of NIPS 2009: Advances in Neural Information Processing Systems (2009) 22: 1105-1113	abstract
Schrauwen et al 2009	Schrauwen, B., Buesing, L. and Legenstein, R.	On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing	In Proc. of NIPS 2008, Advances in Neural Information Processing Systems, volume 20. MIT Press, 2009	fulltext