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Showing 1–9 of 9 results
Advanced filters: Author: Wulfram Gerstner Clear advanced filters

  • A framework based on actor–critic temporal difference learning and employing a biologically plausible network architecture that mimics reward-based learning on memristors and enables full in-memory training for navigation tasks is discussed.

    • Kevin Portner
    • Till Zellweger
    • Alexandros Emboras
    ResearchOpen Access
    Nature Machine Intelligence
    Volume: 7, P: 1939-1953

  • Individual performance during learning is known to be affected by stress and motivation, as well as by genetic predispositions that influence sensitivity to these factors. Here, the authors find that a reinforcement-learning model can provide an integrative framework for predicting the influence of these factors on mouse learning behavior.

    • Gediminas Luksys
    • Wulfram Gerstner
    • Carmen Sandi
    Research
    Nature Neuroscience
    Volume: 12, P: 1180-1186

  • To address challenges of training spiking neural networks (SNNs) at scale, the authors propose a scalable, approximation-free training method for deep SNNs using time-to-first-spike coding. They demonstrate enhanced performance and energy efficiency for neuromorphic hardware.

    • Ana Stanojevic
    • Stanisław Woźniak
    • Wulfram Gerstner
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-13

  • How the ‘what’, ‘where’, and ‘when’ of past experiences are stored in episodic memories and retrieved for suitable decisions remains unclear. In an effort to address these questions, the authors present computational models of neural networks that behave like food caching birds in episodic memory tasks.

    • Johanni Brea
    • Nicola S. Clayton
    • Wulfram Gerstner
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-14

  • The brain exhibits a diversity of plasticity mechanisms across different timecales that constitute the putative basis for learning and memory. Here, the authors demonstrate how these different plasticity mechanisms are orchestrated to support the formation of robust and stable neural cell assemblies.

    • Friedemann Zenke
    • Everton J. Agnes
    • Wulfram Gerstner
    ResearchOpen Access
    Nature Communications
    Volume: 6, P: 1-13

  • Using a combination of computational modeling and electrophysiological recordings, the authors show that the dynamics of spike-frequency adaptation have the effect of temporally decorrelating incoming signals. This decorrelation makes for more energy-efficient information transfer in the CNS.

    • Christian Pozzorini
    • Richard Naud
    • Wulfram Gerstner
    Research
    Nature Neuroscience
    Volume: 16, P: 942-948

  • The authors develop a model of synaptic plasticity that can account for a large body of experimental data on connection patterns in the cortex. This model uses multiple parameters, including presynaptic spike interval and postsynaptic membrane potential.

    • Claudia Clopath
    • Lars Büsing
    • Wulfram Gerstner
    Research
    Nature Neuroscience
    Volume: 13, P: 344-352