Publications

Export 8 results:
Sort by: [ Author (Asc)] Title Type Year
Filters: Author is R. Legenstein  [Clear All Filters]
K
Klampfl, S., R. Legenstein, and W. Maass, "Spiking neurons can learn to solve information bottleneck problems and to extract independent components", Neural Computation, vol. 21, no. 4, pp. 911-959, 2009. Abstract
Klampfl, S., R. Legenstein, and W. Maass, "Information bottleneck optimization and independent component extraction with spiking neurons", NIPS 2006: Advances in Neural Information Processing Systems, vol. 19: MIT Press, pp. 713-720, 2007, 2006. Abstract
L
Legenstein, R., and W. Maass, "What makes a dynamical system computationally powerful?", New Directions in Statistical Signal Processing: From System to Brains: MIT Press, pp. 127-154, 2007. Abstract
Legenstein, R., and W. Maass, "Edge of chaos and prediction of computational performance for neural microcircuit models", Neural Networks, vol. 20, no. 3, pp. 323-334, 2007. Abstract
Legenstein, R., D. Pecevski, and W. Maass, "A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback", PLoS Computational Biology, vol. 4, no. 10, pp. 1-27, 2008. Abstract
Legenstein, R., H. Markram, and W. Maass, "Input prediction and autonomous movement analysis in recurrent circuits of spiking neurons", Reviews in the Neurosciences (Special Issue on Neuroinformatics of Neural and Artificial Computation), vol. 14, no. 1-2, pp. 5-19, 2003. Abstract
M
Maass, W., R. Legenstein, and H. Markram, "A new approach towards vision suggested by biologically realistic neural microcircuit models", Biologically Motivated Computer Vision BMCV 2002, vol. 2525: Springer, pp. 282-293, 2002. Abstract
Maass, W., R. Legenstein, and N. Bertschinger, "Methods for estimating the computational power and generalization capability of neural microcircuits", NIPS 2004: Advances in Neural Information Processing Systems, vol. 17: MIT Press, pp. 865-872, 2005, 2004. Abstract