Robert Legenstein's Publications
This list is also available as BiBTeX file.
- [28]
- R. Legenstein and W. Maass.
Branch-specific plasticity enables
self-organization of nonlinear computation in single neurons.
The Journal of Neuroscience, 31(30):10787-10802, 2011.
(PDF).
- [27]
- R. Legenstein, N. Wilbert, and
L. Wiskott.
Reinforcement learning on slow
features of high-dimensional input streams.
PLoS Computational Biology, 6(8):e1000894, 2010.
(PDF).
- [26]
- M. Jahrer, A. Töscher, and
R. Legenstein.
Combining predictions for accurate
recommender systems.
In KDD '10: Proceedings of the 16th ACM SIGKDD international conference
on Knowledge discovery and data mining, pages 693-702, New York, NY,
USA, 2010. ACM.
(PDF, 362 KB).
- [25]
- R. Legenstein, S. M. Chase, A. B.
Schwartz, and W. Maass.
A reward-modulated Hebbian learning
rule can explain experimentally observed network reorganization in a brain
control task.
The Journal of Neuroscience, 30(25):8400-8410, 2010.
(PDF, 718 KB).
- [24]
- R. Legenstein, S. A. Chase, A. B.
Schwartz, and W. Maass.
Functional network reorganization in
motor cortex can be explained by reward-modulated Hebbian learning.
In Proc. of NIPS 2009: Advances in Neural Information Processing
Systems, D. Koller, D. Schuurmans, Y. Bengio, and L. Bottou, editors,
volume 22, pages 1105-1113. MIT Press, 2010.
(PDF, 246 KB).
- [23]
- L. Buesing, B. Schrauwen, and
R. Legenstein.
Connectivity, dynamics, and memory in
reservoir computing with binary and analog neurons.
Neural Computation, 22(5):1272-1311, 2010.
(PDF, 1094 KB).
- [22]
- B. Schrauwen, L. Buesing, and
R. Legenstein.
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, pages 1425-1432. MIT Press, 2009.
(PDF, 265 KB).
- [22b]
- B. Schrauwen, L. Buesing, and
R. Legenstein.
Supplementary material to: 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.
in press.
(PDF, 184 KB).
- [21]
- Andreas Toescher, Michael Jahrer, and
Robert Legenstein.
Improved neighborhood-based algorithms
for large-scale recommender systems.
In KDD-Cup and Workshop. ACM, 2008.
in press.
(PDF, 121 KB).
- [20]
- R. Legenstein, D. Pecevski, and
W. Maass.
A learning theory for
reward-modulated spike-timing-dependent plasticity with application to
biofeedback.
PLoS Computational Biology, 4(10):1-27, 2008.
(PDF, 1209 KB).
- [20b]
- R. Legenstein, D. Pecevski, and
W. Maass.
Supplementary information to: "A learning theory for reward-modulated
spike-timing-dependent plasticity with application to biofeedback".
PLoS Computational Biology, 4(10), 2008.
(PDF, 1451 KB).
- [19]
- R. Legenstein, D. Pecevski, and
W. Maass.
Theoretical analysis of learning with
reward-modulated spike-timing-dependent plasticity.
In Proc. of NIPS 2007, Advances in Neural Information Processing
Systems, volume 20, pages 881-888. MIT Press, 2008.
(PDF, 199 KB).
- [18]
- S. Klampfl, R. Legenstein, and
W. Maass.
Spiking neurons can learn to solve
information bottleneck problems and extract independent components.
Neural Computation, 21(4):911-959, 2009.
(PDF, 1088 KB).
- [17]
- R. Legenstein and W. Maass.
On the classification capability of
sign-constrained perceptrons.
Neural Computation, 20(1):288-309, 2008.
(PDF, 671 KB).
- [16]
- S. Klampfl, R. Legenstein, and
W. Maass.
Information bottleneck optimization and
independent component extraction with spiking neurons.
In Proc. of NIPS 2006, Advances in Neural Information Processing
Systems, volume 19, pages 713-720. MIT Press, 2007.
(PDF, 613 KB).
- [15]
- R. Legenstein and W. Maass.
Edge of chaos and prediction of
computational performance for neural circuit models.
Neural Networks, 20(3):323-334, 2007.
(PDF, 1480 KB).
- [14]
- R. Legenstein and W. Maass.
What makes a dynamical system
computationally powerful?.
In New Directions in Statistical Signal Processing: From Systems to
Brains, S. Haykin, J. C. Principe, T.J. Sejnowski, and J.G. McWhirter,
editors, pages 127-154. MIT Press, 2007.
(PDF, 582 KB).
- [13]
- R. Legenstein, C. Naeger, and
W. Maass.
What can a neuron learn with
spike-timing-dependent plasticity?.
Neural Computation, 17(11):2337-2382, 2005.
(PDF, 549 KB).
- [13a]
- R. Legenstein and W. Maass.
Additional material to the paper: What can a neuron learn with
spike-timing-dependent plasticity?
Technical report, Institute for Theoretical Computer Science, Graz University
of Technology, 2004.
.
(PDF)
- [12]
- R. Legenstein and W. Maass.
A criterion for the convergence of
learning with spike timing dependent plasticity.
In Advances in Neural Information Processing Systems, Y. Weiss,
B. Schoelkopf, and J. Platt, editors, volume 18, pages 763-770. MIT Press,
2006.
(PDF, 194 KB).
- [11]
- T. Natschlaeger, N. Bertschinger,
and R. Legenstein.
At the edge of chaos: Real-time
computations and self-organized criticality in recurrent neural networks.
In Advances in Neural Information Processing Systems 17,
Lawrence K. Saul, Yair Weiss, and Léon Bottou, editors, pages 145-152.
MIT Press, Cambridge, MA, 2005.
(PDF, 706 KB).
- [10]
- W. Maass, R. Legenstein, and
N. Bertschinger.
Methods for estimating the computational
power and generalization capability of neural microcircuits.
In Advances in Neural Information Processing Systems, L. K. Saul,
Y. Weiss, and L. Bottou, editors, volume 17, pages 865-872. MIT Press, 2005.
(PDF, 196 KB).
- [9]
- R. A. Legenstein and W. Maass.
Wire length as a circuit complexity
measure.
Journal of Computer and System Sciences, 70:53-72, 2005.
(PDF, 372 KB).
- [8]
- R. Legenstein, 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), 14(1-2):5-19, 2003.
(PDF, 179 KB).
- [7]
- W. Maass, R. Legenstein, and H. Markram.
A new approach towards vision suggested by
biologically realistic neural microcircuit models.
In Biologically Motivated Computer Vision. Proc. of the Second
International Workshop, BMCV 2002, Tuebingen, Germany, November 22-24,
2002, H. H. Buelthoff, S. W. Lee, T. A. Poggio, and C. Wallraven,
editors, volume 2525 of Lecture Notes in Computer Science, pages
282-293. Springer (Berlin), 2002.
(PDF, 238 KB).
- [6]
- R. A. Legenstein.
The Wire-Length Complexity of
Neural Networks.
PhD thesis, Graz University of Technology, 2002.
(Gzipped PostScript, 147 p., 1685 KB).
(PDF, 2551 KB).
- [5]
- R. A. Legenstein and W. Maass.
Neural circuits for pattern
recognition with small total wire length.
Theoretical Computer Science, 287:239-249, 2002.
(Gzipped PostScript, 18 p., 51 KB).
(PDF, 129 KB).
- [4]
- R. A. Legenstein.
On the complexity of knock-knee channel
routing with 3-terminal nets.
Technical Report, 2002.
(Gzipped PostScript, 24 p., 91 KB).
- [3]
- R. A. Legenstein and W. Maass.
Optimizing the layout of a balanced
tree.
Technical Report, 2001.
(Gzipped PostScript, 22 p., 93 KB).
(PDF, 247 KB).
- [2]
- R. A. Legenstein and W. Maass.
Foundations for a circuit complexity
theory of sensory processing.
In Proc. of NIPS 2000, Advances in Neural Information Processing
Systems, T. K. Leen, T. G. Dietterich, and V. Tresp, editors,
volume 13, pages 259-265, Cambridge, 2001. MIT Press.
(Gzipped PostScript, 7 p., 40 KB).
(PDF, 94 KB).
The poster presented at NIPS is available as gzipped Postscript.
- [1]
- R. A. Legenstein.
Effizientes Layout von Neuronalen Netzen.
Master's thesis, Technische Universitaet Graz, September 1999.
(Gzipped PostScript, 77 p., 207 KB).
- [-]
- R. Legenstein, S. A. Chase, A. B.
Schwartz, and W. Maass.
A model for learning effects in motor
cortex that may facilitate the brain control of neuroprosthetic devices.
38th Annual Conference of the Society for Neuroscience, Program
517.6, 2008.
- [-]
- R. Legenstein and W. Maass.
An integrated learning rule for
branch strength potentiation and STDP.
39th Annual Conference of the Society for Neuroscience, Program 895.20,
Poster HH36, 2009.