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Johannes Schemmel

Showing results (1-10 of 36) with videos related to

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Frontiers in Neuroscience|April 10, 2024
Closing the loop: High-speed robotics with accelerated neuromorphic hardwareYannik Stradmann, Johannes Schemmel
IEEE Transactions on Neural Networks and Learning Systems|December 30, 2020
The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural NetworksBenjamin Cramer, Yannik Stradmann, Johannes Schemmel, et al.
Neural Computation|September 22, 2007
Spike-frequency adapting neural ensembles: beyond mean adaptation and renewal theoriesEilif Muller, Lars Buesing, Johannes Schemmel, et al.
Open Research Europe|December 13, 2024
Parametrizing analog multi-compartment neurons with genetic algorithmsRaphael Stock, Jakob Kaiser, Eric Müller, et al.
Frontiers in Neuroscience|September 26, 2013
Reward-based learning under hardware constraints-using a RISC processor embedded in a neuromorphic substrateSimon Friedmann, Nicolas Frémaux, Johannes Schemmel, et al.
Trends in Neuroscience and Education|March 20, 2025
Lu.i - A low-cost electronic neuron for education and outreachYannik Stradmann, Julian Göltz, Mihai A Petrovici, et al.
Physical Review. E|November 15, 2016
Stochastic inference with spiking neurons in the high-conductance stateMihai A Petrovici, Johannes Bill, Ilja Bytschok, et al.
Frontiers in Neuroinformatics|June 30, 2009
Establishing a novel modeling tool: a python-based interface for a neuromorphic hardware systemDaniel Brüderle, Eric Müller, Andrew Davison, et al.
Neuroscience|August 24, 2021
Emulating Dendritic Computing Paradigms on Analog Neuromorphic HardwareJakob Kaiser, Sebastian Billaudelle, Eric Müller, et al.
Frontiers in Computational Neuroscience|October 30, 2010
Compensating Inhomogeneities of Neuromorphic VLSI Devices Via Short-Term Synaptic PlasticityJohannes Bill, Klaus Schuch, Daniel Brüderle, et al.
Pageof 4

Showing results (1-10 of 36) with videos related to

Sort By:
Pageof 4
Frontiers in Neuroscience|April 10, 2024
Closing the loop: High-speed robotics with accelerated neuromorphic hardwareYannik Stradmann, Johannes Schemmel
IEEE Transactions on Neural Networks and Learning Systems|December 30, 2020
The Heidelberg Spiking Data Sets for the Systematic Evaluation of Spiking Neural NetworksBenjamin Cramer, Yannik Stradmann, Johannes Schemmel, et al.
Neural Computation|September 22, 2007
Spike-frequency adapting neural ensembles: beyond mean adaptation and renewal theoriesEilif Muller, Lars Buesing, Johannes Schemmel, et al.
Open Research Europe|December 13, 2024
Parametrizing analog multi-compartment neurons with genetic algorithmsRaphael Stock, Jakob Kaiser, Eric Müller, et al.
Frontiers in Neuroscience|September 26, 2013
Reward-based learning under hardware constraints-using a RISC processor embedded in a neuromorphic substrateSimon Friedmann, Nicolas Frémaux, Johannes Schemmel, et al.
Trends in Neuroscience and Education|March 20, 2025
Lu.i - A low-cost electronic neuron for education and outreachYannik Stradmann, Julian Göltz, Mihai A Petrovici, et al.
Physical Review. E|November 15, 2016
Stochastic inference with spiking neurons in the high-conductance stateMihai A Petrovici, Johannes Bill, Ilja Bytschok, et al.
Frontiers in Neuroinformatics|June 30, 2009
Establishing a novel modeling tool: a python-based interface for a neuromorphic hardware systemDaniel Brüderle, Eric Müller, Andrew Davison, et al.
Neuroscience|August 24, 2021
Emulating Dendritic Computing Paradigms on Analog Neuromorphic HardwareJakob Kaiser, Sebastian Billaudelle, Eric Müller, et al.
Frontiers in Computational Neuroscience|October 30, 2010
Compensating Inhomogeneities of Neuromorphic VLSI Devices Via Short-Term Synaptic PlasticityJohannes Bill, Klaus Schuch, Daniel Brüderle, et al.
Pageof 4