Artificial neural networks have been used as a powerful processing tool in various areas
such as pattern recognition, control, robotics, and bioinformatics. Their wide applicability has …

Spiking neural network (SNN) is a new generation of artificial neural networks (ANNs),
which is more analogous with the brain. It has been widely considered with neural …

A vast majority of computation in the brain is performed by spiking neural networks. Despite
the ubiquity of such spiking, we currently lack an understanding of how biological spiking …

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 …

Finding spike-based learning algorithms that can be implemented within the local
constraints of neuromorphic systems, while achieving high accuracy, remains a formidable …

Backpropagation is almost universally used to train artificial neural networks. However, there
are several reasons that backpropagation could not be plausibly implemented by biological …

In this survey, we examine algorithms for conducting credit assignment in artificial neural
networks that are inspired or motivated by neurobiology. These processes are unified under …

Equilibrium Propagation (EP) is an algorithm intrinsically adapted to the training of physical
networks, thanks to the local updates of weights given by the internal dynamics of the …

Stochastic gradient descent with backpropagation is the workhorse of artificial neural
networks. It has long been recognized that backpropagation fails to be a biologically …

This work establishes that a physical system can perform statistical learning without gradient
computations, via an Agnostic Equilibrium Propagation (Aeqprop) procedure that combines …