Neural mechanisms underlying spatial working memory are not fully understood. In this manuscript, simultaneous bilateral monkey recordings and attractor networks show how weakly coupled, redundant pre...

A comprehensive map of the human motor cortex at single-neuron resolution is described.

Neurons in the brain are often many synapses away from motoneurons, yet if a movement results in error, each distant neuron needs a teacher that considers its specific contribution to production of th...

Cortical circuits prioritize diversity over categorical structure, supporting a computational regime geared towards high-dimensional, highly separable neural representations.

Zimnik et al. provide a new, interpretable, unsupervised dimensionality approach for neural population activity. Their approach, sparse component analysis (SCA), reveals the latent factors correspondi...

Shared trial-to-trial variability across sensory neurons is reliably reduced when perceptual performance improves, yet this variability is low dime...

ML & Mathematics

Learned behaviors often generalize across contexts even though neural representations can vary substantially between environments. How the brain preserves task-relevant information across these changi...

Busch et al. use nonlinear neural manifolds to help humans gain rapid control over a noninvasive brain–computer interface, allowing them to learn how to play a video game with real-time fMRI neurofeed...

Songbirds are essential animal models for studying neuronal and behavioral mechanisms of learned vocalizations. Bengalese finch (Lonchura striata domestica) songs contain a limited number of acoustica...

This study identifies a spatiotemporal continuum of structural connectivity development along the sensorimotor-association connectional axis during youth and further demonstrates its implications in b...

To solve diverse real-world tasks, the brain must flexibly switch between task rules and adjust computations. Recent advances in analyzing neural data and modeling neural networks have revealed their ...

The hippocampus exhibits a CA3-to-CA1 coding transformation that combines fast learning with an efficient, compressed neural code.

Artificial intelligence stands poised to transform our society, yet we hardly understand how it works. A synthesis of physics, neuroscience, and AI can fulfill an urgent need: to build a new, unified ...

Hippocampal–neocortical communication reconfigures predetermined circuit motifs to flexibly encode experiences.

These models can partly generalize across species, brain regions and tasks, suggesting that a set of machine-learnable rules govern neural population activity. But will we be able to understand them?

In this issue of Neuron, Pezon et al. introduce neural circuit models with flexible connectivity structure that can generate low-dimensional population dynamics with different distributions of single-...

In the hippocampus, complex processing of sensory stimuli occurs even in the unconscious state.

Context-dependent behavior selects actions according to task demands. Using direct brain recordings in humans, Binish et al. uncover how coordinated population activity efficiently channels informatio...

Nature Neuroscience - Context-dependent behavior selects actions according to task demands. Using direct brain recordings in humans, Binish et al. uncover how coordinated population activity...

We explored how to measure the similarity between two complex systems when they are driven by external inputs, like biological neural circuits or reinforcement learning agents. Our novel method, calle...

We show you colors. You recreate them from memory. Challenge friends to beat your score. It's harder than you think. Play free at dialed.gg.

Physicists have succeeded for the first time in transporting the most expensive and most volatile substance on Earth — antimatter.

YouTube video by Cosyne Talks

The population doctrine—the view that populations, not individual neurons, constitute the fundamental unit of computation—has been gaining ground for years.

Proceedings of the National Academy of Sciences (PNAS), a peer reviewed journal of the National Academy of Sciences (NAS) - an authoritative source of high-impact, original research that broadly spans...

How aperiodic 1/f noise drives ripple activity in human brain and impacts on ripple detections is not fully understood. Here authors show that ripple detections should be driven by the 1/f noise, whic...

Translating neurophysiological findings requires understanding the relationship between common measures of brain activity in animals (spiking activity) and humans (local field potentials, LFP). Prior ...

Reduced rank regression (RRR) is a statistical method for finding a low-dimensional linear mapping between a set of high-dimensional inputs and outputs. In recent years, RRR has found numerous applica...

Epilepsy constitutes a clinically manifest excitability disorder that is characterized by aberrant electrophysiological activity in the electroencephalogram (EEG). The correct identification of the se...