Neuromorphic computing technologies will be important for the future of computing, but
much of the work in neuromorphic computing has focused on hardware development. Here …

Learning is traditionally studied in biological or computational systems. The power of
learning frameworks in solving hard inverse problems provides an appealing case for the …

Deep-learning models have become pervasive tools in science and engineering. However,
their energy requirements now increasingly limit their scalability. Deep-learning …

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 …

In typical artificial neural networks, neurons adjust according to global calculations of a
central processor, but in the brain, neurons and synapses self-adjust based on local …

Ising machines, which are hardware implementations of the Ising model of coupled spins,
have been influential in the development of unsupervised learning algorithms at the origins …

The fields of brain‐inspired computing, robotics, and, more broadly, artificial intelligence (AI)
seek to implement knowledge gleaned from the natural world into human‐designed …

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 …

Memristor-based Pavlov associative memory circuit presented today only realizes the simple
condition reflex process. The secondary condition reflex endows the simple condition reflex …

Equilibrium Propagation is a biologically-inspired algorithm that trains convergent recurrent
neural networks with a local learning rule. This approach constitutes a major lead to allow …