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Angelika Steger

Showing results (1-10 of 13) with videos related to

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Biological Cybernetics|April 23, 2017
Note on the coefficient of variations of neuronal spike trainsJohannes Lengler, Angelika Steger
Frontiers in Computational Neuroscience|November 27, 2014
A high-capacity model for one shot association learning in the brainHafsteinn Einarsson, Johannes Lengler, Angelika Steger
Elife|October 18, 2021
Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemmaSimon Schug, Frederik Benzing, Angelika Steger
Plos One|December 11, 2013
Reliable neuronal systems: the importance of heterogeneityJohannes Lengler, Florian Jug, Angelika Steger
Frontiers in Computational Neuroscience|March 6, 2020
Adaptive Tuning Curve Widths Improve Sample Efficient LearningFlorian Meier, Raphaël Dang-Nhu, Angelika Steger
Neural Computation|March 24, 2017
Multiassociative Memory: Recurrent Synapses Increase Storage CapacityMarcelo Matheus Gauy, Florian Meier, Angelika Steger
Neural Computation|September 17, 2019
Mutual Inhibition with Few Inhibitory Cells via Nonlinear Inhibitory Synaptic InteractionFelix Weissenberger, Marcelo Matheus Gauy, Xun Zou, et al.
Frontiers in Computational Neuroscience|May 31, 2017
A Model of Fast Hebbian Spike Latency NormalizationHafsteinn Einarsson, Marcelo M Gauy, Johannes Lengler, et al.
International Journal of Neural Systems|October 7, 2017
Long Synfire Chains Emerge by Spike-Timing Dependent Plasticity Modulated by Population ActivityFelix Weissenberger, Florian Meier, Johannes Lengler, et al.
Scientific Reports|March 17, 2018
Voltage dependence of synaptic plasticity is essential for rate based learning with short stimuliFelix Weissenberger, Marcelo Matheus Gauy, Johannes Lengler, et al.
Pageof 2

Showing results (1-10 of 13) with videos related to

Sort By:
Pageof 2
Biological Cybernetics|April 23, 2017
Note on the coefficient of variations of neuronal spike trainsJohannes Lengler, Angelika Steger
Frontiers in Computational Neuroscience|November 27, 2014
A high-capacity model for one shot association learning in the brainHafsteinn Einarsson, Johannes Lengler, Angelika Steger
Elife|October 18, 2021
Presynaptic stochasticity improves energy efficiency and helps alleviate the stability-plasticity dilemmaSimon Schug, Frederik Benzing, Angelika Steger
Plos One|December 11, 2013
Reliable neuronal systems: the importance of heterogeneityJohannes Lengler, Florian Jug, Angelika Steger
Frontiers in Computational Neuroscience|March 6, 2020
Adaptive Tuning Curve Widths Improve Sample Efficient LearningFlorian Meier, Raphaël Dang-Nhu, Angelika Steger
Neural Computation|March 24, 2017
Multiassociative Memory: Recurrent Synapses Increase Storage CapacityMarcelo Matheus Gauy, Florian Meier, Angelika Steger
Neural Computation|September 17, 2019
Mutual Inhibition with Few Inhibitory Cells via Nonlinear Inhibitory Synaptic InteractionFelix Weissenberger, Marcelo Matheus Gauy, Xun Zou, et al.
Frontiers in Computational Neuroscience|May 31, 2017
A Model of Fast Hebbian Spike Latency NormalizationHafsteinn Einarsson, Marcelo M Gauy, Johannes Lengler, et al.
International Journal of Neural Systems|October 7, 2017
Long Synfire Chains Emerge by Spike-Timing Dependent Plasticity Modulated by Population ActivityFelix Weissenberger, Florian Meier, Johannes Lengler, et al.
Scientific Reports|March 17, 2018
Voltage dependence of synaptic plasticity is essential for rate based learning with short stimuliFelix Weissenberger, Marcelo Matheus Gauy, Johannes Lengler, et al.
Pageof 2