Search

The International Brain Laboratory presents a brain-wide electrophysiological map obtained from pooling data from 12 laboratories that performed the same standardized perceptual decision-making task in mice.

How the navigational system of the brain constructs spatial maps that require both rapid changes and representational accuracy is explored.

Two-photon microscopy across the fly brain using sensors that permit simultaneous measurement of neural activity and metabolic flux reveals global and local coordination of neural activity and energy metabolism.

CA1 neurons encode an animal's position within the environment. Here the authors identified in hippocampal neuronal activity a detrimental type of noise that limits accuracy of spatial position, relative to body size.

The mouse neocortex supports sensory performance through transient increases in sensory coding redundancy, neural codes that are robust to cellular variability, and inter-area fluctuation modes that transmit sensory data and task responses in non-interfering channels.

Using a combination of in vivo recording and theoretical modeling, Campbell et al. develop a framework for understanding the integration of self-motion and landmark cues in entorhinal cortex and demonstrate that these principles extend to behavior.

A microscopy system that enables simultaneous recording from hundreds of neurons in the mouse visual cortex reveals that the brain enhances its coding capacity by representing visual inputs in dimensions perpendicular to correlated noise.

The authors propose a new framework, deep evolutionary reinforcement learning, evolves agents with diverse morphologies to learn hard locomotion and manipulation tasks in complex environments, and reveals insights into relations between environmental physics, embodied intelligence, and the evolution of rapid learning.

Lightning Pose is an efficient pose estimation approach that requires few labeled training data owing to its semi-supervised learning strategy and ensembling.

A modular architecture for managing and sharing electrophysiology, behavior, colony management and other data has been built to support individual laboratories or large consortia.

One of the ambitions of computational neuroscience is that we will continue to make improvements in the field of artificial intelligence that will be informed by advances in our understanding of how the brains of various species evolved to process information. To that end, here the authors propose an expanded version of the Turing test that involves embodied sensorimotor interactions with the world as a new framework for accelerating progress in artificial intelligence.

A deep network is best understood in terms of components used to design it—objective functions, architecture and learning rules—rather than unit-by-unit computation. Richards et al. argue that this inspires fruitful approaches to systems neuroscience.

In this Perspective, the authors propose that functional insights into generalist cortical computation may reside at the level of population patterns rather than functionally defined cell types. They then review results showing that medial entorhinal cortex (MEC) neurons exhibit substantial heterogeneity, suggesting MEC is a generalist circuit that computes diverse episodic states.