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

Publications associées (50)

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Theory of representation learning in cortical neural networks

Carlos Stein Naves de Brito

Our brain continuously self-organizes to construct and maintain an internal representation of the world based on the information arriving through sensory stimuli. Remarkably, cortical areas related to different sensory modalities appear to share the same f ...

EPFL2016

Learning with Surprise

Mohammadjavad Faraji

Everybody knows what it feels to be surprised. Surprise raises our attention and is crucial for learning. It is a ubiquitous concept whose traces have been found in both neuroscience and machine learning. However, a comprehensive theory has not yet been de ...

EPFL2016

Human and Machine Learning in Non-Markovian Decision Making

Michael Herzog, Aaron Michael Clarke, Elisa Tartaglia, Silvia Marchesotti, Walter Senn

Humans can learn under a wide variety of feedback conditions. Reinforcement learning (RL), where a series of rewarded decisions must be made, is a particularly important type of learning. Computational and behavioral studies of RL have focused mainly on Ma ...

Public Library of Science2015

Stochastic variational learning in recurrent spiking networks

Wulfram Gerstner

The ability to learn and perform statistical inference with biologically plausible recurrent networks of spiking neurons is an important step toward understanding perception and reasoning. Here we derive and investigate a new learning rule for recurrent sp ...

Frontiers Research Foundation2014

High Bandwidth Synaptic Communication and Frequency Tracking in Human Neocortex

Michele Giugliano

Neuronal firing, synaptic transmission, and its plasticity form the building blocks for processing and storage of information in the brain. It is unknown whether adult human synapses are more efficient in transferring information between neurons than roden ...

Public Library Science2014

Models of Reward-Modulated Spike-Timing-Dependent Plasticity

Nicolas Frémaux

How do animals learn to repeat behaviors that lead to the obtention of food or other “rewarding” objects? As a biologically plausible paradigm for learning in spiking neural networks, spike-timing dependent plasticity (STDP) has been shown to perform well ...

EPFL2013

Perceptual Learning, Roving, and Synaptic Drift

Michael Herzog, Aaron Michael Clarke

Perceptual learning improves with most basic stimuli. Interestingly, performance does not improve when stimuli of two types are randomly presented during training (roving). For example, there is no perceptual learning when left or right bisection stimuli w ...

2012

Variational Learning for Recurrent Spiking Networks

Wulfram Gerstner, Danilo Jimenez Rezende, Daniël Pieter Wierstra

We derive a plausible learning rule updating the synaptic efficacies for feedforward, feedback and lateral connections between observed and latent neurons. Operating in the context of a generative model for distributions of spike sequences, the learning me ...

2011

Robust Bayesian reinforcement learning through tight lower bounds

Christos Dimitrakakis

In the Bayesian approach to sequential decision making, exact calculation of the (subjective) utility is intractable. This extends to most special cases of interest, such as reinforcement learning problems. While utility bounds are known to exist for this ...

2011

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