Equilibrium systems are a powerful way to express neural computations. As special cases,
they include models of great current interest in both neuroscience and machine learning …

The response time of physical computational elements is finite, and neurons are no
exception. In hierarchical models of cortical networks each layer thus introduces a response …

Perceptual constancy refers to the fact that the perceived geometrical and physical
characteristics of objects remain constant despite transformations of the objects such as rigid …

We propose a method for extending the technique of equilibrium propagation for estimating
gradients in fixed-point neural networks to the more general setting of directed, time-varying …

Dendrites are root-like extensions from the neuron cell body and have long been thought to
serve as the predominant input structures of neurons. Since the early twentieth century …

Inspired by the recent success of deep learning [1], several models emerged trying to
explain how the brain might realize plasticity rules reaching similar performances as deep …

To achieve their goal of realizing fast and energy-efficient learning, neuromorphic systems
require computationally powerful models that obey the constraints imposed by a physical …