Publications by Nicolas Frémaux

Neuromodulated-Spike-Timing-Dependent Pasticity, and Theory of Three-Factor Learning Rules

Classical Hebbian learning puts the emphasis on joint pre- and postsynaptic activity, but neglects the potential role of neuromodulators. Since neuromodulators convey information about novelty or reward, the influence of neuromodulators on synaptic plastic ...

Frontiers Research Foundation2016

Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

Wulfram Gerstner, Nicolas Frémaux

Animals repeat rewarded behaviors, but the physiological basis of reward-based learning has only been partially elucidated. On one hand, experimental evidence shows that the neuromodulator dopamine carries information about rewards and affects synaptic pla ...

Public Library of Science2013

Perceptual learning, roving and the unsupervised bias

Michael Herzog, Wulfram Gerstner, Carl Kristoffer Aberg, Nicolas Frémaux

Perceptual learning improves perception through training. Perceptual learning improves with most stimulus types but fails when certain stimulus types are mixed during training (roving). This result is surprising because classical supervised and unsupervise ...

Elsevier2012

Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity

Wulfram Gerstner, Nicolas Frémaux

Recent experiments have shown that spike-timing-dependent plasticity is influenced by neuromodulation. We derive theoretical conditions for successful learning of reward-related behavior for a large class of learning rules where Hebbian synaptic plasticity ...

Society for Neuroscience2010

Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail

Wulfram Gerstner, Eleni Vasilaki, Nicolas Frémaux, Walter Senn

Changes of synaptic connections between neurons are thought to be the physiological basis of learning. These changes can be gated by neuromodulators that encode the presence of reward. We study a family of reward-modulated synaptic learning rules for spiki ...

Public Library of Science2009

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Nicolas Frémaux

Neuromodulated-Spike-Timing-Dependent Pasticity, and Theory of Three-Factor Learning Rules

Models of Reward-Modulated Spike-Timing-Dependent Plasticity

Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

Perceptual learning, roving and the unsupervised bias

Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity

Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail

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Chat with Graph Search

Neuromodulated-Spike-Timing-Dependent Pasticity, and Theory of Three-Factor Learning Rules

Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons

Models of Reward-Modulated Spike-Timing-Dependent Plasticity

Functional Requirements for Reward-Modulated Spike-Timing-Dependent Plasticity

Perceptual learning, roving and the unsupervised bias

Spike-Based Reinforcement Learning in Continuous State and Action Space: When Policy Gradient Methods Fail