@article {68938, title = {Learning produces an orthogonalized state machine in the hippocampus.}, journal = {Nature}, volume = {640}, year = {2025}, month = {2025 February 12}, abstract = {

Cognitive maps confer animals with flexible intelligence by representing spatial, temporal and abstract relationships that can be used to shape thought, planning and behaviour. Cognitive maps have been observed in the hippocampus1, but their algorithmic form and learning mechanisms remain obscure. Here we used large-scale, longitudinal two-photon calcium imaging to record activity from thousands of neurons in the CA1 region of the hippocampus while mice learned to efficiently collect rewards from two subtly different linear tracks in virtual reality. Throughout learning, both animal behaviour and hippocampal neural activity progressed through multiple stages, gradually revealing improved task representation that mirrored improved behavioural efficiency. The learning process involved progressive decorrelations in initially similar hippocampal neural activity within and across tracks, ultimately resulting in orthogonalized representations resembling a state machine capturing the inherent structure of the task. This decorrelation process was driven by individual neurons acquiring task-state-specific responses (that is, {\textquoteright}state cells{\textquoteright}). Although various standard artificial neural networks did not naturally capture these dynamics, the clone-structured causal graph, a hidden Markov model variant, uniquely reproduced both the final orthogonalized states and the learning trajectory seen in animals. The observed cellular and population dynamics constrain the mechanisms underlying cognitive map formation in the hippocampus, pointing to hidden state inference as a fundamental computational principle, with implications for both biological and artificial intelligence.

}, doi = {10.1038/s41586-024-08548-w}, url = {https://doi.org/10.1038/s41586-024-08548-w}, author = {Sun, Weinan and Winnubst, Johan and Natrajan, Maanasa and Lai, Chongxi and Kajikawa, Koichiro and Michaelos, Michalis and Gattoni, Rachel and Stringer, Carsen and Flickinger, Daniel and Fitzgerald, James E and Spruston, Nelson} } @article {65897, title = {Cellpose: a generalist algorithm for cellular segmentation.}, journal = {Nature Methods}, volume = {18}, year = {2021}, month = {2021 Jan 07}, pages = {100-106}, abstract = {

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. Cellpose was trained on a new dataset of highly varied images of cells, containing over 70,000 segmented objects. We also demonstrate a three-dimensional (3D) extension of Cellpose that reuses the two-dimensional (2D) model and does not require 3D-labeled data. To support community contributions to the training data, we developed software for manual labeling and for curation of the automated results. Periodically retraining the model on the community-contributed data will ensure that Cellpose improves constantly.

}, issn = {1548-7105}, doi = {10.1038/s41592-020-01018-x}, author = {Stringer, Carsen and Wang, Tim and Michaelos, Michalis and Pachitariu, Marius} } @article {67234, title = {High-precision coding in visual cortex.}, journal = {Cell}, volume = {184}, year = {2021}, month = {2021 May 13}, pages = {2767-78}, abstract = {

Individual neurons in visual cortex provide the brain with unreliable estimates of visual features. It is not known whether the single-neuron variability is correlated across large neural populations, thus impairing the global encoding of stimuli. We recorded simultaneously from up to 50,000 neurons in mouse primary visual cortex (V1) and in higher order visual areas and measured stimulus discrimination thresholds of 0.35{\textdegree} and 0.37{\textdegree}, respectively, in an orientation decoding task. These neural thresholds were almost 100 times smaller than the behavioral discrimination thresholds reported in mice. This discrepancy could not be explained by stimulus properties or arousal states. Furthermore, behavioral variability during a sensory discrimination task could not be explained by neural variability in V1. Instead, behavior-related neural activity arose dynamically across a network of non-sensory brain areas. These results imply that perceptual discrimination in mice is limited by downstream decoders, not by neural noise in sensory representations.

}, issn = {1097-4172}, doi = {10.1016/j.cell.2021.03.042}, author = {Stringer, Carsen and Michaelos, Michalis and Tsyboulski, Dmitri and Lindo, Sarah E and Pachitariu, Marius} } @article {65294, title = {Cellpose: a generalist algorithm for cellular segmentation}, journal = {bioRxiv}, year = {2020}, month = {2020 Feb 03}, abstract = {

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 algorithm called Cellpose, which can very precisely segment a wide range of image types out-of-the-box 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. 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.

}, doi = {10.1101/2020.02.02.931238}, author = {Stringer, Carsen and Michaelos, Michalis and Pachitariu, Marius} } @article {65752, title = {High precision coding in visual cortex}, journal = {BioRxiv}, year = {2019}, month = {06/2019}, pages = {679324}, abstract = {

Single neurons in visual cortex provide unreliable measurements of visual features due to their high trial-to-trial variability. It is not known if this {\textquotedblleft}noise{\textquotedblright} extends its effects over large neural populations to impair the global encoding of sensory stimuli. We recorded simultaneously from \~{}20,000 neurons in mouse visual cortex and found that the neural population had discrimination thresholds of 0.3{\textdegree} in an orientation decoding task. These thresholds are \~{}100 times smaller than those reported behaviorally in mice. The discrepancy between neural and behavioral discrimination could not be explained by the types of stimuli we used, by behavioral states or by the sequential nature of trial-by-trial perceptual learning tasks. These results imply that the limits of sensory perception in mice are not set by neural noise in sensory cortex, but by the limitations of downstream decoders.

}, doi = {https://doi.org/10.1101/679324}, author = {Stringer, Carsen and Michaelos, Michalis and Pachitariu, Marius} }