Neuromodulators in the brain act globally at many forms of synaptic plasticity, represented
as metaplasticity, which is rarely considered by existing spiking (SNNs) and nonspiking …

Equilibrium propagation (EP) is an alternative to backpropagation (BP) that allows the
training of deep neural networks with local learning rules. It thus provides a compelling …

This paper focuses on training implicit models of infinite layers. Specifically, previous works
employ implicit differentiation and solve the exact gradient for the backward propagation …

Using backpropagation to compute gradients of objective functions for optimization has
remained a mainstay of machine learning. Backpropagation, or reverse-mode differentiation …

Many synaptic plasticity rules found in natural circuits have not been incorporated into
artificial neural networks (ANNs). We showed that incorporating a nonlocal feature of …

While Moore's law has driven exponential computing power expectations, its nearing end
calls for new avenues for improving the overall system performance. One of these avenues …

Indeed, information geometry, IG is an ever-growing area with a great scope of applications
ranging from Probability & Statistics, Machine Learning (ML), Artificial Intelligence (AI) …

For both humans and machines, the essence of learning is to pinpoint which components in
its information processing pipeline are responsible for an error in its output, a challenge that …

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 …

While Moore's law has driven exponential computing power expectations, its nearing end
calls for new avenues for improving the overall system performance. One of these avenues …