Legenstein R - Search Results

1

The location of the axon initial segment affects the bandwidth of spike initiation dynamics.

Verbist C, Müller MG, Mansvelder HD, Legenstein R, Giugliano M. Verbist C, et al. Among authors: legenstein r. PLoS Comput Biol. 2020 Jul 23;16(7):e1008087. doi: 10.1371/journal.pcbi.1008087. eCollection 2020 Jul. PLoS Comput Biol. 2020. PMID: 32701953 Free PMC article.

2

A learning theory for reward-modulated spike-timing-dependent plasticity with application to biofeedback.

Legenstein R, Pecevski D, Maass W. Legenstein R, et al. PLoS Comput Biol. 2008 Oct;4(10):e1000180. doi: 10.1371/journal.pcbi.1000180. Epub 2008 Oct 10. PLoS Comput Biol. 2008. PMID: 18846203 Free PMC article.

3

Reinforcement learning on slow features of high-dimensional input streams.

Legenstein R, Wilbert N, Wiskott L. Legenstein R, et al. PLoS Comput Biol. 2010 Aug 19;6(8):e1000894. doi: 10.1371/journal.pcbi.1000894. PLoS Comput Biol. 2010. PMID: 20808883 Free PMC article.

4

Ensembles of spiking neurons with noise support optimal probabilistic inference in a dynamically changing environment.

Legenstein R, Maass W. Legenstein R, et al. PLoS Comput Biol. 2014 Oct 23;10(10):e1003859. doi: 10.1371/journal.pcbi.1003859. eCollection 2014 Oct. PLoS Comput Biol. 2014. PMID: 25340749 Free PMC article.

5

Network Plasticity as Bayesian Inference.

Kappel D, Habenschuss S, Legenstein R, Maass W. Kappel D, et al. Among authors: legenstein r. PLoS Comput Biol. 2015 Nov 6;11(11):e1004485. doi: 10.1371/journal.pcbi.1004485. eCollection 2015 Nov. PLoS Comput Biol. 2015. PMID: 26545099 Free PMC article.

6

A Model for Structured Information Representation in Neural Networks of the Brain.

Müller MG, Papadimitriou CH, Maass W, Legenstein R. Müller MG, et al. Among authors: legenstein r. eNeuro. 2020 May 29;7(3):ENEURO.0533-19.2020. doi: 10.1523/ENEURO.0533-19.2020. Print 2020 May/Jun. eNeuro. 2020. PMID: 32381648 Free PMC article.

7

Spike frequency adaptation supports network computations on temporally dispersed information.

Salaj D, Subramoney A, Kraisnikovic C, Bellec G, Legenstein R, Maass W. Salaj D, et al. Among authors: legenstein r. Elife. 2021 Jul 26;10:e65459. doi: 10.7554/eLife.65459. Elife. 2021. PMID: 34310281 Free PMC article.

8

Cortical oscillations support sampling-based computations in spiking neural networks.

Korcsak-Gorzo A, Müller MG, Baumbach A, Leng L, Breitwieser OJ, van Albada SJ, Senn W, Meier K, Legenstein R, Petrovici MA. Korcsak-Gorzo A, et al. Among authors: legenstein r. PLoS Comput Biol. 2022 Mar 24;18(3):e1009753. doi: 10.1371/journal.pcbi.1009753. eCollection 2022 Mar. PLoS Comput Biol. 2022. PMID: 35324886 Free PMC article.

9

What can a neuron learn with spike-timing-dependent plasticity?

Legenstein R, Naeger C, Maass W. Legenstein R, et al. Neural Comput. 2005 Nov;17(11):2337-82. doi: 10.1162/0899766054796888. Neural Comput. 2005. PMID: 16156932

10

Connectivity, dynamics, and memory in reservoir computing with binary and analog neurons.

Büsing L, Schrauwen B, Legenstein R. Büsing L, et al. Among authors: legenstein r. Neural Comput. 2010 May;22(5):1272-311. doi: 10.1162/neco.2009.01-09-947. Neural Comput. 2010. PMID: 20028227

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