Carsen Stringer

The paper develops two methods to correct axial-motion artifacts in two-photon calcium imaging using suite2p-in-depth, improving fluorescence measurements in awake mice.

This paper introduces a new system for activating and recording thousands of neurons over a large field-of-view with pixel-level precision.

The paper develops an AI framework to analyze blood scans and predict patient outcomes by characterizing tumor-immune ecosystems in circulation.

The paper shows that large-scale neural recordings in mice exhibit dynamical properties similar to those of a critically normalized random symmetric matrix, suggesting a critical initialization for biological neural networks.

The paper links neural representations to behavior by testing mice's responses to new stimuli and finding that neural discrimination correlates with behavioral performance in specific visual areas.

The paper develops WARP, a platform integrating neuronal activity and gene expression data in zebrafish brains, revealing functional roles of molecularly defined cell populations and their activity correlations.

Suite2p processes large-scale neural recordings, outperforming existing methods in motion correction and cell detection.

This study shows that orofacial movements, not eye movements, primarily drive neural activity in mouse visual cortex during free viewing.

This study shows high-frequency fMRI signals are linked to brain states and can predict spontaneous neural activity using machine learning.

This paper predicts EEG neural rhythms from fMRI data across sleep and wakefulness using machine learning, identifying large-scale network patterns associated with alpha and delta rhythms.

The paper tests whether mismatch responses in visual cortex are due to spatial or temporal predictive coding by manipulating visual stimuli in mice.

Cellpose's flow-field output on SAM's pretrained encoder yields one segmenter that errs less than a second human annotator on out-of-distribution images (0.163 vs 0.257 error rate).

This paper introduces cycleHCR technology for high-resolution spatial transcriptomics and protein imaging in thick tissues, demonstrating its application in mouse embryos and brain slices.

Cellpose3 improves cellular segmentation in noisy, blurry, or undersampled microscopy images through one-click image restoration tools.

Rastermap is a visualization method for neural data that sorts neurons along a one-dimensional manifold to reveal activity patterns in high-dimensional recordings.

The paper simplifies artificial neural network models of primary visual cortex while maintaining predictive power, using data from mouse and monkey V1 neurons.

The paper uses spatial single-cell analysis to uncover molecular and structural development of human fetal cortex, revealing early layer and area specification.

The paper benchmarks cellular segmentation methods, showing Cellpose outperforms Mediar when tested fairly and a transformer-based variant of Cellpose does not improve performance.

The paper investigates whether perceptual learning in mice is due to supervised or unsupervised learning by analyzing neural activity in visual cortex during task learning and unrewarded exposure.

Facemap models neural activity using orofacial tracking, improving prediction accuracy and explaining more variance in visual cortex neurons.

The paper shows that learning in the hippocampus produces orthogonalized neural representations resembling a state machine, captured by HMMs and biologically plausible networks, but not by gradient-based models.

The paper introduces WHaloCaMP, a near-infrared calcium indicator, and demonstrates its use in imaging calcium responses in various organisms.

Kilosort4 swaps K-means-style clustering for a bipartite modularity graph method with a merging tree, and recovers most ground-truth neurons in simulations where competing sorters lose half of them.

Cellpose 2.0 enables training specialist segmentation models with minimal user effort and data, achieving state-of-the-art results on new image categories.

The paper measures neural population coding precision in mouse visual cortex and finds that discrimination thresholds are much lower than behavioral thresholds, suggesting downstream decoding limitations.

Cellpose segments diverse 2D and 3D microscopy cell images without retraining by predicting topological gradient vector flows via a style-conditioned residual U-net and routing pixels to cell centers.

The paper analyzes the correlation structure of natural image coding in visual cortical populations of awake mice, finding a power-law decay in principal component variance that reflects coding smoothness.

The paper shows that spontaneous brain activity encodes multidimensional behavioral information across the forebrain, alongside sensory input in orthogonal dimensions.

Suite2p processes two-photon calcium movies faster than real time on desktop workstations, roughly doubling curated cell yield over CNMF and recovering 13,451 neurons in one session.

This study compares spike deconvolution methods for calcium imaging and finds that non-negative deconvolution (NND) is the most accurate and efficient approach.