Research output per year
Research output per year
- 28 results
Search results
-
2005
At the edge of chaos: Real-time computations and self-organized criticality in recurrent neural networks
Natschläger, T., Bertschinger, N. & Legenstein, R. A., 2005, Advances in Neural Information Processing Systems. Cambridge: MIT Press, p. 145-152Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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Methods for estimating the computational power and generalization capability of neural microcircuits
Maass, W., Legenstein, R. A. & Bertschinger, N., 2005, Advances in Neural Information Processing Systems. MIT Press, p. 865-872Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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2006
A criterion for the convergence of learning with spike timing dependent plasticity
Legenstein, R. A. & Maass, W., 2006, Advances in Neural Information Processing Systems. MIT Press, p. 763-770Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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2007
Information Bottleneck Optimization and Independent Component Extraction with Spiking Neurons
Klampfl, S., Legenstein, R. A. & Maass, W., 2007, Advances in Neural Information Processing Systems. Cambridge, MA: MIT Press, p. 713-720Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Open AccessFile -
2008
A model for learning effects in motor cortex that may facilitate the brain control of neuroprosthetic devices
Legenstein, R., Chase, S. M., Schwartz, A. B. & Maass, W., 2008, (Accepted/In press) 2008 Neuroscience Meeting Planner, Online. .Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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Improved neighborhood-based algorithms for large-scale recommender systems
Legenstein, R., 2008, (Accepted/In press) KDD-Cup and Workshop. ACM, 2008. .Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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Theoretical analysis of learning with reward-modulated spike-timing-dependent plasticity
Legenstein, R. A., Pecevski, D. & Maass, W., 2008, (Accepted/In press) Proc. of NIPS 2007, Advances in Neural Information Processing Systems. ., Vol. 20.Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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2009
On Computational Power and the Order-Chaos Phase Transition in Reservoir Computing
Schrauwen, B., Büsing, L. H. & Legenstein, R., 2009, (Accepted/In press) Annual Conference on Neural Information Processing Systems. .Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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2010
Combining predictions for accurate recommender systems
Legenstein, R., 2010, Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. ., p. 693-702Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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Functional network reorganization in motor cortex can be explained by reward-modulated Hebbian learning
Legenstein, R., Chase, S., Schwartz, A. B. & Maass, W., 2010, Proc. of NIPS 2009, Advances in Neural Information Processing Systems. MIT Press, p. 1105-1113Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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2011
Dendritic computation could support probabilistic inference in networks of spiking neurons
Legenstein, R., 2011, Abstracts Annual Meeting. ., p. 1-1Research output: Chapter in Book/Report/Conference proceeding › Conference paper
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Eliminating the teacher in reservoir computing
Hörzer, G. M., Legenstein, R. & Maass, W., 2011, Proceedings of the 2nd International Conference on Morphological Computation. ., p. 32-32Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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2015
Synaptic sampling: A Bayesian approach to neural network plasticity and rewiring
Kappel, D., Habenschuss, S., Legenstein, R. & Maass, W., 2015, (Accepted/In press) Proceedings of NIPS. .Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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2016
Variable Binding through Assemblies in Spiking Neural Networks
Legenstein, R., Papadimitriou, C. H., Vempala, S. S. & Maass, W., 2016, CEUR Workshop Proceedings. Vol. 1773. 6 p.Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Open Access -
2017
Neuromorphic hardware in the loop: Training a deep spiking network on the BrainScaleS wafer-scale system
Schmitt, S., Klahn, J., Bellec, G., Grubl, A., Guttler, M., Hartel, A., Hartmann, S., Husmann, D., Husmann, K., Jeltsch, S., Karasenko, V., Kleider, M., Koke, C., Kononov, A., Mauch, C., Muller, E., Muller, P., Partzsch, J., Petrovici, M. A., Schiefer, S., & 9 others, 30 Jun 2017, 2017 International Joint Conference on Neural Networks, IJCNN 2017 - Proceedings. Institute of Electrical and Electronics Engineers, p. 2227-2234 8 p. 7966125. (Proceedings of the International Joint Conference on Neural Networks; vol. 2017-May).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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Pattern representation and recognition with accelerated analog neuromorphic systems
Petrovici, M. A., Schmitt, S., Klähn, J., Stöckel, D., Schroeder, A., Bellec, G., Bill, J., Breitwieser, O., Bytschok, I., Grübl, A., Güttler, M., Hartel, A., Hartmann, S., Husmann, D., Husmann, K., Jeltsch, S., Karasenko, V., Kleider, M., Koke, C., Kononov, A., & 15 others, 17 Mar 2017, Proceedings - IEEE International Symposium on Circuits and Systems. Institute of Electrical and Electronics Engineers, 4 p. 8050530Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Open AccessFile -
2018
Long short-term memory and learning-to-learn in networks of spiking neurons
Bellec, G. E. F., Salaj, D., Subramoney, A., Legenstein, R. & Maass, W., 2018, Advances in Neural Information Processing Systems: NeurIPS.Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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Long Term Memory and the Densest K-Subgraph Problem
Legenstein, R., Maass, W., Papapdimitriou, C. H. & Vempala, S. S., 2018, 9th Innovations in Theoretical Computer Science Conference (ITCS 2018). Schloss Dagstuhl - Leibniz-Zentrum für Informatik, Vol. 94. p. 57:1–57:15 57. (LIPIcs-Leibniz International Proceedings in Informatics ).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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2020
H-Mem: Harnessing synaptic plasticity with Hebbian memory networks
Limbacher, T. & Legenstein, R., 2020, Advances in Neural Information Processing Systems. Vol. 33.Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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2021
Dynamic action inference with recurrent spiking neural networks
Traub, M., Butz, M. V., Legenstein, R. & Otte, S., 2021, Artificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings. Farkaš, I., Masulli, P., Otte, S. & Wermter, S. (eds.). p. 233-244 12 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 12895 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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Many-joint robot arm control with recurrent spiking neural networks
Traub, M., Legenstein, R. & Otte, S., 2021, IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2021. p. 4918-4925 8 p.Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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Training adversarially robust sparse networks via Bayesian connectivity sampling
Özdenizci, O. & Legenstein, R., 2021, Proceedings of Machine Learning Research: PMLR. Vol. 139. p. 8314-8324Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Open Access -
2022
Improving robustness against stealthy weight bit-flip attacks by output code matching
Özdenizci, O. & Legenstein, R., 2022, Proceedings - 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022. p. 13378-13387 10 p. (Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition; vol. 2022-June).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Open Access -
2023
Context-dependent computations in spiking neural networks with apical modulation
Ferrand, R., Baronig, M., Limbacher, T. & Legenstein, R., 2023, Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings: 32nd International Conference on Artificial Neural Networks, Heraklion, Crete, Greece, September 26–29, 2023, Proceedings, Part I. Iliadis, L., Papaleonidas, A., Angelov, P. & Jayne, C. (eds.). Cham: Springer, p. 381-392 12 p. (Lecture Notes in Computer Science; vol. 14254).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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Fault Pruning: Robust Training of Neural Networks with Memristive Weights
Kraisnikovic, C., Stathopoulos, S., Prodromakis, T. & Legenstein, R., 2023, Unconventional Computation and Natural Computation, UCNC 2023. Genova, D. & Kari, J. (eds.). Cham: Springer, p. 124-139 16 p. (Lecture Notes in Computer Science; vol. 14003).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
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Generating conceptual architectural 3D geometries with denoising diffusion models
Sebestyen, A., Özdenizci, O., Legenstein, R. & Hirschberg, U. L., 2023, eCAADe 2023 - Digital Design Reconsidered: Proceedings of the 41st eCAADe Conference, 18-23 September, Graz University of Technology. Dokonal, W., Hirschberg, U. & Wurzer, G. (eds.). Vol. 2. p. 451-460Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Open Access -
Generating Conceptual Architectural 3D Geometries with Denoising Diffusion Models: Showcasing a deep learning based 3D generative prototype
Sebestyen, A., Özdenizci, O., Legenstein, R. & Hirschberg, U., 2023, eCAADe 2023 - Digital Design Reconsidered: Proceedings of the International Conference on Education and Research in Computer Aided Architectural Design in Europe. Dokonal, W., Hirschberg, U., Wurzer, G. & Wurzer, G. (eds.). eCAADe (Education and Research in Computer Aided Architectural Design in Europe), Vol. 2. p. 451-460 10 p.Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review
Open Access -
Interaction of Generalization and Out-of-Distribution Detection Capabilities in Deep Neural Networks
Klaiber Aboitiz, F. J., Legenstein, R. & Özdenizci, O., 2023, Artificial Neural Networks and Machine Learning – ICANN 2023 - 32nd International Conference on Artificial Neural Networks, Proceedings. Iliadis, L., Papaleonidas, A., Angelov, P. & Jayne, C. (eds.). Cham: Springer, p. 248-259 12 p. (Lecture Notes in Computer Science; vol. 14263).Research output: Chapter in Book/Report/Conference proceeding › Conference paper › peer-review