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

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Publications associées à Reinforcement Learning Using a Continuous Time Actor-Critic Framework with Spiking Neurons | EPFL Graph Search

Deep Learning Theory Through the Lens of Diagonal Linear Networks

Scott William Pesme

In this PhD manuscript, we explore optimisation phenomena which occur in complex neural networks through the lens of

2

-layer diagonal linear networks. This rudimentary architecture, which consists of a two layer feedforward linear network with a diagonal ...

EPFL2024

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

Graph Chatbot

Chattez avec Graph Search

Unveiling the complexity of learning and decision-making

Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills

Subcortical correlates of consciousness with human single neuron recordings

Fast adaptation to rule switching using neuronal surprise

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Fear learning induces synaptic potentiation between engram neurons in the rat lateral amygdala

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Deep Learning Theory Through the Lens of Diagonal Linear Networks

Impact of Mitofusin 2 in the Nucleus Accumbens on motivated behavior and underlying neurobiological mechanisms

Non-invasive stimulation of the human striatum disrupts reinforcement learning of motor skills

Unveiling the complexity of learning and decision-making

Fast adaptation to rule switching using neuronal surprise

Subcortical correlates of consciousness with human single neuron recordings

Seeking the new, learning from the unexpected: Computational models of surprise and novelty in the brain

Fear learning induces synaptic potentiation between engram neurons in the rat lateral amygdala

Predicting the long-term collective behaviour of fish pairs with deep learning

Incorporating Projective Geometry into Deep Learning

Impact of Mitofusin 2 in the Nucleus Accumbens on motivated behavior and underlying neurobiological mechanisms

Deep Learning Theory Through the Lens of Diagonal Linear Networks