Summary Paragraph Sensory, motor, and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures1,2. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution but from only a few dozen neurons per shank. Optical Ca2+ imaging3–5 offers more coverage but lacks the temporal resolution to reliably distinguish individual spikes and does not measure local field potentials. To date, no technology compatible with unrestrained animals has combined high spatiotemporal resolution with large volume coverage. To satisfy this need, we designed, fabricated, and tested a new silicon probe called Neuropixels. Each probe has 384 recording channels that can programmably address 960 CMOS processing-compatible low-impedance TiN6 sites that tile a single 10 mm long, 70x20 µm cross section shank. The 6x9 mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed, and digitized on the base, allowing noise-free digital data transmission directly from the probe. The combination of dense recording sites and high channel count yielded well-isolated spiking activity from hundreds of neurons per probe implanted in mice and rats. Using two probes, more than 700 well-isolated single neurons were simultaneously recorded from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed recording large populations of neurons from multiple brain structures in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens the path to record brain-wide neural activity during behavior.

Neuronal populations in sensory cortex produce variable responses to sensory stimuli, and exhibit intricate spontaneous activity even without external sensory input. Cortical variability and spontaneous activity have been variously proposed to represent random noise, recall of prior experience, or encoding of ongoing behavioral and cognitive variables. Recording over 10,000 neurons in mouse visual cortex, we observed that spontaneous activity reliably encoded a high-dimensional latent state, which was partially related to the mouse’s ongoing behavior and was represented not just in visual cortex but across the forebrain. Sensory inputs did not interrupt this ongoing signal, but added onto it a representation of external stimuli in orthogonal dimensions. Thus, visual cortical population activity, despite its apparently noisy structure, reliably encodes an orthogonal fusion of sensory and multidimensional behavioral information.

† correspondence to (stringerc, pachitarium) @ janelia.hhmi.org Many biological applications require the segmentation of cell bodies, membranes and nuclei from microscopy images. Deep learning has enabled great progress on this problem, but current methods are specialized for images that have large training datasets. Here we introduce a generalist, deep learning-based segmentation method called Cellpose, which can precisely segment cells from a wide range of image types and does not require model retraining or parameter adjustments. We trained Cellpose on a new dataset of highly-varied images of cells, containing over 70,000 segmented objects. We also demonstrate a 3D extension of Cellpose which reuses the 2D model and does not require 3D-labelled data. To support community contributions to the training data, we developed software for manual labelling and for curation of the automated results, with optional direct upload to our data repository. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.

Two-photon microscopy of calcium-dependent sensors has enabled unprecedented recordings from vast populations of neurons. While the sensors and microscopes have matured over several generations of development, computational methods to process the resulting movies remain inefficient and can give results that are hard to interpret. Here we introduce Suite2p: a fast, accurate and complete pipeline that registers raw movies, detects active cells, extracts their calcium traces and infers their spike times. Suite2p runs on standard workstations, operates faster than real time, and recovers ~2 times more cells than the previous state-of-the-art method. Its low computational load allows routine detection of ~10,000 cells simultaneously with standard twophoton resonant-scanning microscopes. Recordings at this scale promise to reveal the fine structure of activity in large populations of neurons or large populations of subcellular structures such as synaptic boutons.

A neuronal population encodes information most efficiently when its stimulus responses are highdimensional and uncorrelated, and most robustly when they are correlated and lower-dimensional. Here, we analyzed the dimensionality of the encoding of natural images by large visual cortical populations recorded from awake mice. Evoked population activity was high dimensional, with correlations obeying an unexpected power-law: the n th principal component variance scaled as 1/n. This scaling was not inherited from the 1/f spectrum of natural images, because it persisted after stimulus whitening. We proved mathematically that if the variance spectrum decayed any slower, the population code could not be smooth, allowing small changes in input to dominate population activity. The theory also predicts larger power-law exponents for lower-dimensional stimulus ensembles, which we validated experimentally. These results suggest that coding smoothness may represent a fundamental constraint governing correlations in neural population codes.