@article {69228, title = {Unsupervised pretraining in biological neural networks}, journal = {Nature}, year = {2025}, month = {2025 Jun 18}, abstract = {

Representation learning in neural networks may be implemented with supervised or unsupervised algorithms, distinguished by the availability of instruction. In the sensory cortex, perceptual learning drives neural plasticity1-13, but it is not known whether this is due to supervised or unsupervised learning. Here we recorded populations of up to 90,000 neurons simultaneously from the primary visual cortex (V1) and higher visual areas (HVAs) while mice learned multiple tasks, as well as during unrewarded exposure to the same stimuli. Similar to previous studies, we found that neural changes in task mice were correlated with their behavioural learning. However, the neural changes were mostly replicated in mice with unrewarded exposure, suggesting that the changes were in fact due to unsupervised learning. The neural plasticity was highest in the medial HVAs and obeyed visual, rather than spatial, learning rules. In task mice only, we found a ramping reward-prediction signal in anterior HVAs, potentially involved in supervised learning. Our neural results predict that unsupervised learning may accelerate subsequent task learning, a prediction that we validated with behavioural experiments.

Preprint:\ https://www.biorxiv.org/content/early/2024/02/27/2024.02.25.581990

}, doi = {10.1038/s41586-025-09180-y}, url = {https://doi.org/10.1038/s41586-025-09180-y}, author = {Lin Zhong and Scott Baptista and Rachel Gattoni and Jon Arnold and Daniel Flickinger and Carsen Stringer and Marius Pachitariu} } @article {69173, title = {Distributed fMRI dynamics predict distinct EEG rhythms across sleep and wakefulness.}, journal = {bioRxiv}, year = {2024}, month = {2024 Jan 30}, abstract = {

The brain exhibits rich oscillatory dynamics that vary across tasks and states, such as the EEG oscillations that define sleep. These oscillations play critical roles in cognition and arousal, but the brainwide mechanisms underlying them are not yet described. Using simultaneous EEG and fast fMRI in subjects drifting between sleep and wakefulness, we developed a machine learning approach to investigate which brainwide fMRI dynamics predict alpha (8-12 Hz) and delta (1-4 Hz) rhythms. We predicted moment-by-moment EEG power from fMRI activity in held-out subjects, and found that information about alpha power was represented by a remarkably small set of regions, segregated in two distinct networks linked to arousal and visual systems. Conversely, delta rhythms were diffusely represented on a large spatial scale across the cortex. These results identify distributed networks that predict delta and alpha rhythms, and establish a computational framework for investigating fMRI brainwide dynamics underlying EEG oscillations.

}, doi = {10.1101/2024.01.29.577429}, url = {https://doi.org/10.1101/2024.01.29.577429}, author = {Leandro P. L. Jacob and Sydney M. Bailes and Stephanie D. Williams and Carsen Stringer and Laura D. Lewis} } @article {69349, title = {Transformers do not outperform Cellpose}, journal = {bioRxiv}, year = {2024}, month = {2024 Apr 7}, abstract = {

In a recent publication, Ma et al [1] claim that a transformer-based cellular segmentation method called Mediar [2] {\textemdash} which won a Neurips challenge {\textemdash} outperforms Cellpose [3] (0.897 vs 0.543 median F1 score). Here we show that this result was obtained by artificially impairing Cellpose in multiple ways. When we removed these impairments, Cellpose outperformed Mediar (0.861 vs 0.826 median F1 score on the updated test set). To further investigate the performance of transformers for cellular segmentation, we replaced the Cellpose backbone with a transformer. The transformer-Cellpose model also did not outperform the standard Cellpose (0.848 median F1 test score). Our results suggest that transformers do not advance the state-of-the-art in cellular segmentation.

}, doi = {10.1101/2024.04.06.587952}, url = {https://www.biorxiv.org/content/early/2024/04/07/2024.04.06.587952}, author = {Carsen Stringer and Marius Pachitariu} } @article {68465, title = {Facemap: a framework for modeling neural activity based on orofacial tracking}, journal = {Nature Neuroscience}, year = {2023}, month = {2023 Nov 20}, abstract = {

Recent studies in mice have shown that orofacial behaviors drive a large fraction of neural activity across the brain. To understand the nature and function of these signals, we need better computational models to characterize the behaviors and relate them to neural activity. Here we developed Facemap, a framework consisting of a keypoint tracking algorithm and a deep neural network encoder for predicting neural activity. We used the Facemap keypoints as input for the deep neural network to predict the activity of \~{}50,000 simultaneously-recorded neurons and in visual cortex we doubled the amount of explained variance compared to previous methods. Our keypoint tracking algorithm was more accurate than existing pose estimation tools, while the inference speed was several times faster, making it a powerful tool for closed-loop behavioral experiments. The Facemap tracker was easy to adapt to data from new labs, requiring as few as 10 annotated frames for near-optimal performance. We used Facemap to find that the neuronal activity clusters which were highly driven by behaviors were more spatially spread-out across cortex. We also found that the deep keypoint features inferred by the model had time-asymmetrical state dynamics that were not apparent in the raw keypoint data. In summary, Facemap provides a stepping stone towards understanding the function of the brainwide neural signals and their relation to behavior.

}, doi = {10.1038/s41593-023-01490-6}, url = {https://doi.org/10.1038/s41593-023-01490-6}, author = {Atika Syeda and Lin Zhong and Renee Tung and Will Long and Marius Pachitariu and Carsen Stringer} } @article {68920, title = {Rastermap: a discovery method for neural population recordings}, journal = {Nat. Neurosci.}, year = {2023}, month = {2024 Oct 16}, abstract = {

Neurophysiology has long progressed through exploratory experiments and chance discoveries. Anecdotes abound of researchers listening to spikes in real time and noticing patterns of activity related to ongoing stimuli or behaviors. With the advent of large-scale recordings, such close observation of data has become difficult. To find patterns in large-scale neural data, we developed {\textquoteright}Rastermap{\textquoteright}, a visualization method that displays neurons as a raster plot after sorting them along a one-dimensional axis based on their activity patterns. We benchmarked Rastermap on realistic simulations and then used it to explore recordings of tens of thousands of neurons from mouse cortex during spontaneous, stimulus-evoked and task-evoked epochs. We also applied Rastermap to whole-brain zebrafish recordings; to wide-field imaging data; to electrophysiological recordings in rat hippocampus, monkey frontal cortex and various cortical and subcortical regions in mice; and to artificial neural networks. Finally, we illustrate high-dimensional scenarios where Rastermap and similar algorithms cannot be used effectively.

}, doi = {10.1038/s41593-024-01783-4}, url = {https://doi.org/10.1038/s41593-024-01783-4}, author = {Carsen Stringer and Lin Zhong and Atika Syeda and Fengtong Du and Marius Pachitariu} } @article {68578, title = {Solving the spike sorting problem with Kilosort}, journal = {bioRxiv}, year = {2023}, month = {2023 Jan 07}, abstract = {

Spike sorting is the computational process of extracting the firing times of single neurons from recordings of local electrical fields. This is an important but hard problem in neuroscience, complicated by the non-stationarity of the recordings and the dense overlap in electrical fields between nearby neurons. To solve the spike sorting problem, we have continuously developed over the past eight years a framework known as Kilosort. This paper describes the various algorithmic steps introduced in different versions of Kilosort. We also report the development of Kilosort4, a new version with substantially improved performance due to new clustering algorithms inspired by graph-based approaches. To test the performance of Kilosort, we developed a realistic simulation framework which uses densely sampled electrical fields from real experiments to generate non-stationary spike waveforms and realistic noise. We find that nearly all versions of Kilosort outperform other algorithms on a variety of simulated conditions, and Kilosort4 performs best in all cases, correctly identifying even neurons with low amplitudes and small spatial extents in high drift conditions.

}, doi = {10.1101/2023.01.07.523036}, url = {https://doi.org/10.1101/2023.01.07.523036}, author = {Marius Pachitariu and Shashwat Sridhar and Carsen Stringer} } @article {67858, title = {The importance of accounting for movement when relating neuronal activity to sensory and cognitive processes.}, journal = {Journal of Neuroscience: The Official Journal of the Society for Neuroscience}, volume = {42}, year = {2022}, month = {2022 Feb 23}, pages = {1375-1382}, abstract = {

A surprising finding of recent studies in mouse is the dominance of widespread\ movement-related\ activity\ throughout the brain, including in early\ sensory\ areas. In awake subjects, failing to\ account\ for\ movement\ risks misattributing\ movement-related\ activity\ to other (e.g.,\ sensory\ or\ cognitive)\ processes. In this article, we 1) review task designs for separating task-related\ and\ movement-related\ activity, 2) review three {\textquoteright}case studies{\textquoteright} in which\ not\ considering\ movement\ would have resulted in critically different interpretations of\ neuronal\ function, and 3) discuss functional couplings that may prevent us from ever fully isolating\ sensory,\ motor, and\ cognitive-related\ activity. Our main thesis is that neural signals\ related\ to\ movement\ are ubiquitous, and therefore ought to be considered first and foremost when attempting to correlate\ neuronal\ activity\ with task-related\ processes.

}, doi = {10.1523/JNEUROSCI.1919-21.2021}, author = {Edward Zagha and Jeffrey C Erlich and Soohyun Lee and Gyorgy Lur and Daniel H O{\textquoteright}Connor and Nicholas A Steinmetz and Carsen Stringer and Hongdian Yang} } @article {68254, title = {Omnipose: a high-precision morphology-independent solution for bacterial cell segmentation}, journal = {bioRxiv}, year = {2022}, month = {2022 Jul 27}, abstract = {

Advances in microscopy hold great promise for allowing quantitative and precise readouts of morphological and molecular phenomena at the single cell level in bacteria. However, the potential of this approach is ultimately limited by the availability of methods to perform unbiased cell segmentation, defined as the ability to faithfully identify cells independent of their morphology or optical characteristics. In this study, we present a new algorithm, Omnipose, which accurately segments samples that present significant challenges to current algorithms, including mixed bacterial cultures, antibiotic-treated cells, and cells of extended or branched morphology. We show that Omnipose achieves generality and performance beyond leading algorithms and its predecessor, Cellpose, by virtue of unique neural network outputs such as the gradient of the distance field. Finally, we demonstrate the utility of Omnipose in the characterization of extreme morphological phenotypes that arise during interbacterial antagonism and on the segmentation of non-bacterial objects. Our results distinguish Omnipose as a uniquely powerful tool for answering diverse questions in bacterial cell biology.

}, doi = {10.1101/2021.11.03.467199}, url = {https://doi.org/10.1101/2021.11.03.467199}, author = {Kevin J. Cutler and Carsen Stringer and Paul A. Wiggins and Joseph D. Mougous} }