Neuromorphic computing takes inspiration from the brain to create energy-efficient hardware
for information processing, capable of highly sophisticated tasks. Systems built with standard …

In this Review, we describe the singular success of attractor neural network models in
describing how the brain maintains persistent activity states for working memory, corrects …

Sharp wave ripples (SPW‐Rs) represent the most synchronous population pattern in the
mammalian brain. Their excitatory output affects a wide area of the cortex and several …

Understanding the fundamental relationships between physics and its information-
processing capability has been an active research topic for many years. Physical reservoir …

Flexible computation is a hallmark of intelligent behavior. However, little is known about how
neural networks contextually reconfigure for different computations. In the present work, we …

Artificial neural networks (ANNs) are essential tools in machine learning that have drawn
increasing attention in neuroscience. Besides offering powerful techniques for data analysis …

Neurons often respond to diverse combinations of task-relevant variables. This form of
mixed selectivity plays an important computational role which is related to the dimensionality …

A major shift is happening within neurophysiology: a population doctrine is drawing level
with the single-neuron doctrine that has long dominated the field. Population-level ideas …

This manuscript serves a specific purpose: to give readers from fields such as material
science, chemistry, or electronics an overview of implementing a reservoir computing (RC) …

Timing is critical to most forms of learning, behavior, and sensory-motor processing.
Converging evidence supports the notion that, precisely because of its importance across a …