We show that Langevin Markov chain Monte Carlo inference in an energy-based model with
latent variables has the property that the early steps of inference, starting from a stationary …

We show that Langevin MCMC inference in an energy-based model with latent variables
has the property that the early steps of inference, starting from a stationary point, correspond …

We introduce Equilibrium Propagation, a learning framework for energy-based models. It
involves only one kind of neural computation, performed in both the first phase (when the …

Ongoing advances in experimental technique are making commonplace simultaneous
recordings of the activity of tens to hundreds of cortical neurons at high temporal resolution …

How the brain performs credit assignment is a fundamental unsolved problem in
neuroscience. Manybiologically plausible'algorithms have been proposed, which compute …

Abstract Models of sensory processing and learning in the cortex need to efficiently assign
credit to synapses in all areas. In deep learning, a known solution is error backpropagation …

Experimental evidence indicates that synaptic modification depends on the timing
relationship between the presynaptic inputs and the output spikes that they generate. In this …

Deep learning has seen remarkable developments over the last years, many of them
inspired by neuroscience. However, the main learning mechanism behind these advances …

This review article summarises recently proposed theories on how neural circuits in the
brain could approximate the error back-propagation algorithm used by artificial neural …

Learning and memory in the brain are implemented by complex, time-varying changes in
neural circuitry. The computational rules according to which synaptic weights change over …