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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.

Neural activity in human brains rapidly restructures to reflect hidden relationships needed to adapt to a changing environment. Surprisingly, trial-and-error learning and verbal instruction induce similar changes.

Brain-wide recordings in mice reveal that prior expectations are distributed through recurrent loops across all levels of cortical and subcortical processing.

Path integration abilities, important for spatial navigation, vary widely across individuals and deteriorate in old age. This work shows that path integration errors in general, as well as age-related path integration deficits, are mainly caused by accumulating noise in people’s velocity estimation.

Using a spatial reasoning task in mice, the authors show that retrosplenial cortex encodes spatial hypotheses with well-behaved recurrent dynamics, which can combine these hypotheses with incoming information to resolve ambiguities.

The principle of peak selection is described, by which local interactions and smooth gradients drive self-organization of discrete global modules.

A neocortical–entorhinal–hippocampal network model based on grid cell states recapitulates experimental results and reconciles the spatial, associative and episodic memory roles of the hippocampus.

Attractor network dynamics can support several computations performed by the brain. In their Review, Khona and Fiete introduce different attractor dynamics and their computational utility, describe evidence of attractor networks across the brain and explain how such networks could be recombined to increase their flexibility and versatility.

Measurements of activity in the entorhinal cortex of monkeys indicate that the recruitment of a cognitive map is a key part of mental navigation, and that cognitive maps can support behavior in the absence of external sensory input.

Neural populations often encode unknown variables. Chaudhuri et al. develop a method to decode unknown variables by finding shapes in neural data. They show that a mammalian brain circuit of thousands of neurons constructs a navigational compass with only a one-dimensional ring of stable activity states.

The authors demonstrate that strongly recurrent circuits inferred from neural activity, even with unlimited data from every neuron, are biased. Synapses are inferred between unconnected but correlated neurons. Inference based on non-equilibrium activity may help remedy this.

In mouse brain, neurotensin released into the basolateral amygdala by neurons in the paraventricular nucleus of the thalamus assigns positive or negative valence during associative learning.

Analyses of neural activity of mice competing in a social competition assay monitored by a computer vision tool reveal a neural circuit with a role in dynamically modulating social dominance.

How grid cells’ geometric firing patterns arise remains unknown. This study shows preserved grid cell co-activity patterns across sleep that reflect grid field overlap during waking, suggesting that grid firing emerges from specific connectivity patterns.

Noel et al. show aberrant updating of expectations in three distinct mouse models of autism spectrum disorder. Brain-wide neurophysiology data suggest this stems from excess units encoding deviations from prior mean and a lack of sensory prediction errors in frontal areas.

Mammalian grid cells have a spatially periodic pattern of responses to location, which is a puzzling feature. Here, the authors demonstrate that this pattern of activity is compatible with a coding scheme that allows for very accurate localization.

In this study, the authors show that the spatial responses of populations of grid cells are constrained to a two-dimensional activity manifold, and the relationships between pairs of grid cells are resistant to perturbation. These findings provide evidence of low-dimensional continuous attractor dynamics in the network.

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.

What are the challenges associated with storing information over time in the brain? Here the authors explore the computational principles by which biological memory might be built. They develop a high-level view of shared problems and themes in short- and long-term memory and highlight questions for future research.