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Mathematical models of V1 seek to explain the response properties of V1 neurons, often with more complex models providing more accurate predictions. Here, the authors show that deep neural network models of mouse and monkey V1 can be dramatically simplified to a two-layer “minimodel" while retaining high accuracy.

During visual learning, neural plasticity is driven by unsupervised learning in mice.

Analysis of the encoding of natural images by very large populations of neurons in the visual cortex of awake mice characterizes the high dimensional geometry of the neural responses.

Cellpose3 employs deep-learning-based approaches for image restoration to improve cellular segmentation and shows strong generalized performance even on images degraded by noise, blurring or undersampling.

Facemap is a data analysis framework for tracking keypoints on mouse faces and relating them to large-scale neural activity. Both of these steps use state-of-the-art convolutional neural networks to achieve high precision and fast processing speeds.

Cellpose 2.0 improves cell segmentation by offering pretrained models that can be fine-tuned using a human-in-the-loop training pipeline and fewer than 1,000 user-annotated regions of interest.

Neuropixels 1.0 NHP is a 45-mm, high-density silicon probe capable of recording large numbers of neurons with single-neuron resolution from most areas in a macaque’s brain.

Kilosort4 is a spike-sorting algorithm with improved performance compared to previous versions, owing to the use of a graph-based clustering approach. The tool extracts the activity of individual neurons from electrophysiological recordings acquired with, for example, Neuropixels electrodes.

Simultaneous recordings from hundreds of grid cells in rats, combined with topological data analysis, show that network activity in grid cells resides on a toroidal manifold that is invariant across environments and brain states.

New silicon probes known as Neuropixels are shown to record from hundreds of neurons simultaneously in awake and freely moving rodents.

A large, open dataset containing parallel recordings from six visual cortical and two thalamic areas of the mouse brain is presented, from which the relative timing of activity in response to visual stimuli and behaviour is used to construct a hierarchy scheme that corresponds to anatomical connectivity data.

Silicon probes for electrical recording from neurons usually have fewer wires than recording channels available to carry signals off the probe, which restricts the number of channels that can be recorded simultaneously. The authors propose to pool electrodes, using a single wire to serve many channels through a set of controllable switches.

Cellpose is a generalist, deep learning-based approach for segmenting structures in a wide range of image types. Cellpose does not require parameter adjustment or model retraining and outperforms established methods on 2D and 3D datasets.

Rastermap is an analysis method for exploring dynamical and spatial relationships among hundreds to hundreds of thousands of neurons. The algorithm uses a fast optimization technique to discover complex neural patterns, such as sequences.

Eiselt et al. report conditions under which mice confuse thirst for hunger, similar to some human decisions that lead to over-eating. Evaluation of physiological need state requires consuming food or water and depends on the prefrontal cortex.