Stretchable synaptic transistors, a core technology in neuromorphic electronics, have
functions and structures similar to biological synapses and can concurrently transmit signals …

Hebb postulated that, to store information in the brain, assemblies of excitatory neurons
coding for a percept are bound together via associative long-term synaptic plasticity. In this …

Shifting computing architectures from von Neumann to event-based spiking neural networks
(SNNs) uncovers new opportunities for low-power processing of sensory data in …

A striking difference between brain-inspired neuromorphic processors and current von
Neumann processor architectures is the way in which memory and processing is organized …

Implementing compact, low-power artificial neural processing systems with real-time on-line
learning abilities is still an open challenge. In this paper we present a full-custom mixed …

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 …

The problem of training spiking neural networks (SNNs) is a necessary precondition to
understanding computations within the brain, a field still in its infancy. Previous work has …

Autism spectrum disorder (ASD) is strongly associated with de novo gene mutations. One of
the most commonly affected genes is SCN2A. ASD-associated SCN2A mutations impair the …

Studies of activity-driven nervous system plasticity have primarily focused on the gray matter.
However, MRI-based imaging studies have shown that white matter, primarily composed of …

Specific frequency bands of neural oscillations have been correlated with a range of
cognitive and behavioral effects (eg, memory and attention). The causal role of specific …