Wulfram Gerstner

Wulfram Gerstner in 2018 Wulfram Gerstner (born 1963 in Heilbronn) is a German and Swiss computational neuroscientist. His research focuses on neural spiking patterns in neural networks, and their connection to learning, spatial representation and navigation. Since 2006 Gerstner has been a full professor of Computer Science and Life Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he also serves as a Director of the Laboratory of Computational Neuroscience. Provided by Wikipedia
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  1. 1
    A functional model of adult dentate gyrus neurogenesis

    A functional model of adult dentate gyrus neurogenesis by Olivia Gozel, Wulfram Gerstner

    Published 2021-06-01
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  2. 2
    Coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram.

    Coding and decoding with adapting neurons: a population approach to the peri-stimulus time histogram. by Richard Naud, Wulfram Gerstner

    Published 2012-01-01
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  3. 3
    Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network

    Predicting non-linear dynamics by stable local learning in a recurrent spiking neural network by Aditya Gilra, Wulfram Gerstner

    Published 2017-11-01
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  4. 4
    Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation.

    Nonlinear Hebbian Learning as a Unifying Principle in Receptive Field Formation. by Carlos S N Brito, Wulfram Gerstner

    Published 2016-09-01
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  5. 5
    Stability of working memory in continuous attractor networks under the control of short-term plasticity.

    Stability of working memory in continuous attractor networks under the control of short-term plasticity. by Alexander Seeholzer, Moritz Deger, Wulfram Gerstner

    Published 2019-04-01
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  6. 6
    Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation.

    Excitable neuronal assemblies with adaptation as a building block of brain circuits for velocity-controlled signal propagation. by Hesam Setareh, Moritz Deger, Wulfram Gerstner

    Published 2018-07-01
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  7. 7
    Reinforcement learning using a continuous time actor-critic framework with spiking neurons.

    Reinforcement learning using a continuous time actor-critic framework with spiking neurons. by Nicolas Frémaux, Henning Sprekeler, Wulfram Gerstner

    Published 2013-04-01
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  8. 8
    Optimal Stimulation Protocol in a Bistable Synaptic Consolidation Model

    Optimal Stimulation Protocol in a Bistable Synaptic Consolidation Model by Chiara Gastaldi, Samuel Muscinelli, Wulfram Gerstner

    Published 2019-11-01
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  9. 9
    Computational models of episodic-like memory in food-caching birds

    Computational models of episodic-like memory in food-caching birds by Johanni Brea, Nicola S. Clayton, Wulfram Gerstner

    Published 2023-05-01
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  10. 10
    Synaptic plasticity in neural networks needs homeostasis with a fast rate detector.

    Synaptic plasticity in neural networks needs homeostasis with a fast rate detector. by Friedemann Zenke, Guillaume Hennequin, Wulfram Gerstner

    Published 2013-01-01
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  11. 11
    Voltage and spike timing interact in STDP - a unified model

    Voltage and spike timing interact in STDP - a unified model by Claudia Clopath, Claudia Clopath, Wulfram Gerstner

    Published 2010-07-01
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  12. 12
    Fast adaptation to rule switching using neuronal surprise.

    Fast adaptation to rule switching using neuronal surprise. by Martin L L R Barry, Wulfram Gerstner

    Published 2024-02-01
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  13. 13
    Learning what matters: Synaptic plasticity with invariance to second-order input correlations.

    Learning what matters: Synaptic plasticity with invariance to second-order input correlations. by Carlos Stein Naves de Brito, Wulfram Gerstner

    Published 2024-02-01
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  14. 14
    How single neuron properties shape chaotic dynamics and signal transmission in random neural networks.

    How single neuron properties shape chaotic dynamics and signal transmission in random neural networks. by Samuel P Muscinelli, Wulfram Gerstner, Tilo Schwalger

    Published 2019-06-01
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  15. 15
    STDP in adaptive neurons gives close-to-optimal information transmission

    STDP in adaptive neurons gives close-to-optimal information transmission by Guillaume Hennequin, Wulfram Gerstner, Jean-Pascal ePfister

    Published 2010-12-01
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  16. 16
    Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons.

    Enhanced Sensitivity to Rapid Input Fluctuations by Nonlinear Threshold Dynamics in Neocortical Pyramidal Neurons. by Skander Mensi, Olivier Hagens, Wulfram Gerstner, Christian Pozzorini

    Published 2016-02-01
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  17. 17
    The silent period of evidence integration in fast decision making.

    The silent period of evidence integration in fast decision making. by Johannes Rüter, Henning Sprekeler, Wulfram Gerstner, Michael H Herzog

    Published 2013-01-01
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  18. 18
    A dynamic attractor network model of memory formation, reinforcement and forgetting.

    A dynamic attractor network model of memory formation, reinforcement and forgetting. by Marta Boscaglia, Chiara Gastaldi, Wulfram Gerstner, Rodrigo Quian Quiroga

    Published 2023-12-01
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  19. 19
    On the choice of metric in gradient-based theories of brain function.

    On the choice of metric in gradient-based theories of brain function. by Simone Carlo Surace, Jean-Pascal Pfister, Wulfram Gerstner, Johanni Brea

    Published 2020-04-01
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  20. 20
    Tag-trigger-consolidation: a model of early and late long-term-potentiation and depression.

    Tag-trigger-consolidation: a model of early and late long-term-potentiation and depression. by Claudia Clopath, Lorric Ziegler, Eleni Vasilaki, Lars Büsing, Wulfram Gerstner

    Published 2008-12-01
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