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Robert Legenstein

Showing results (31-40 of 43) with videos related to

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Elife|July 26, 2021
Spike frequency adaptation supports network computations on temporally dispersed informationDarjan Salaj, Anand Subramoney, Ceca Kraisnikovic, et al.
Plos One|August 19, 2015
Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral InhibitionJohannes Bill, Lars Buesing, Stefan Habenschuss, et al.
Frontiers in Neuroscience|May 2, 2022
Classification of Whisker Deflections From Evoked Responses in the Somatosensory Barrel Cortex With Spiking Neural NetworksHorst Petschenig, Marta Bisio, Marta Maschietto, et al.
Neuroscience|October 17, 2021
Dendritic Computing: Branching Deeper into Machine LearningJyotibdha Acharya, Arindam Basu, Robert Legenstein, et al.
Nature Communications|September 30, 2016
Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapsesAlexander Serb, Johannes Bill, Ali Khiat, et al.
Plos Computational Biology|July 24, 2020
The location of the axon initial segment affects the bandwidth of spike initiation dynamicsChristophe Verbist, Michael G Müller, Huibert D Mansvelder, et al.
Cerebral Cortex (New York, N.Y. : 1991)|August 13, 2019
STDP Forms Associations between Memory Traces in Networks of Spiking NeuronsChristoph Pokorny, Matias J Ison, Arjun Rao, et al.
Nature Communications|July 19, 2020
A solution to the learning dilemma for recurrent networks of spiking neuronsGuillaume Bellec, Franz Scherr, Anand Subramoney, et al.
IEEE Transactions on Biomedical Circuits and Systems|April 2, 2019
Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 PrototypeYexin Yan, David Kappel, Felix Neumarker, et al.
Nature Communications|February 1, 2025
Rapid learning with phase-change memory-based in-memory computing through learning-to-learnThomas Ortner, Horst Petschenig, Athanasios Vasilopoulos, et al.
Pageof 5

Showing results (31-40 of 43) with videos related to

Sort By:
Pageof 5
Elife|July 26, 2021
Spike frequency adaptation supports network computations on temporally dispersed informationDarjan Salaj, Anand Subramoney, Ceca Kraisnikovic, et al.
Plos One|August 19, 2015
Distributed Bayesian Computation and Self-Organized Learning in Sheets of Spiking Neurons with Local Lateral InhibitionJohannes Bill, Lars Buesing, Stefan Habenschuss, et al.
Frontiers in Neuroscience|May 2, 2022
Classification of Whisker Deflections From Evoked Responses in the Somatosensory Barrel Cortex With Spiking Neural NetworksHorst Petschenig, Marta Bisio, Marta Maschietto, et al.
Neuroscience|October 17, 2021
Dendritic Computing: Branching Deeper into Machine LearningJyotibdha Acharya, Arindam Basu, Robert Legenstein, et al.
Nature Communications|September 30, 2016
Unsupervised learning in probabilistic neural networks with multi-state metal-oxide memristive synapsesAlexander Serb, Johannes Bill, Ali Khiat, et al.
Plos Computational Biology|July 24, 2020
The location of the axon initial segment affects the bandwidth of spike initiation dynamicsChristophe Verbist, Michael G Müller, Huibert D Mansvelder, et al.
Cerebral Cortex (New York, N.Y. : 1991)|August 13, 2019
STDP Forms Associations between Memory Traces in Networks of Spiking NeuronsChristoph Pokorny, Matias J Ison, Arjun Rao, et al.
Nature Communications|July 19, 2020
A solution to the learning dilemma for recurrent networks of spiking neuronsGuillaume Bellec, Franz Scherr, Anand Subramoney, et al.
IEEE Transactions on Biomedical Circuits and Systems|April 2, 2019
Efficient Reward-Based Structural Plasticity on a SpiNNaker 2 PrototypeYexin Yan, David Kappel, Felix Neumarker, et al.
Nature Communications|February 1, 2025
Rapid learning with phase-change memory-based in-memory computing through learning-to-learnThomas Ortner, Horst Petschenig, Athanasios Vasilopoulos, et al.
Pageof 5