@article {70286, title = {Extracting large-scale neural activity with Suite2p}, journal = {bioRxiv}, year = {2026}, month = {2026 Feb 06}, abstract = {

Neural recordings using optical methods have improved dramatically. For example, we demonstrate here recordings of over 100,000 neurons from the mouse cortex obtained with a standard commercial microscope. To process such large datasets, we developed Suite2p, a collection of efficient algorithms for motion correction, cell detection, activity extraction and quality control. We also developed new approaches to benchmark performance on these tasks. Our GPU-accelerated non-rigid motion correction substantially outperforms alternative methods, while running over five times faster. For cell detection, Suite2p outperforms the CNMF algorithm in Caiman and Fiola, finding more cells and producing fewer false positives, while running in a fraction of the time. We also introduce quality control steps for users to evaluate performance on their own data, while offering alternative algorithms for specialized types of recordings such as those from one-photon and voltage imaging.

}, doi = {10.64898/2026.02.04.703741}, url = {https://www.biorxiv.org/content/early/2026/02/06/2026.02.04.703741}, author = {Stringer, Carsen and Ki, Chris and DelGrosso, Nicholas and LaFosse, Paul and Zhang, Qingqing and Pachitariu, Marius} } @article {70246, title = {High performance sorting of motor unit action potentials with EMUsort}, journal = {bioRxiv}, year = {2026}, month = {2026 Jan 07}, abstract = {

Understanding how neural signals control muscle activity during behavior is a key challenge in motor neuroscience. To this end, recent advances in intramuscular multielectrode arrays have enabled high-quality multichannel recordings of many motor unit action potentials (MUAPs) in freely moving subjects. However, identifying individual MUAP events within multichannel recordings is a significant challenge for existing spike sorting methods, which are typically optimized for identifying action potentials from neurons in the brain. To overcome this challenge, we developed the Enhanced Motor Unit sorter (EMUsort), an extension of Kilosort4 (KS4) that achieves high-performance MUAP spike sorting. We applied EMUsort to high-resolution intramuscular recordings from rat forelimb during locomotion and monkey forelimb during a reaching task. EMUsort improves upon prior methods by addressing key challenges encountered with MUAP datasets, including: 1) long time delays across electrodes due to propagation along muscle fibers, 2) more complex waveform shapes compared to neuronal action potentials, and 3) a high degree of MUAP overlap due to cumulative motor unit recruitment. We compared EMUsort to existing spike sorting methods quantitatively using simulated datasets that closely emulated the rat and monkey datasets we recorded. EMUsort provided median error rate reductions of 67.5\% and 49.9\% during periods of high motor unit activation for the rat and monkey datasets, respectively. In sum, EMUsort provides a substantial improvement to MUAP spike sorter accuracy, especially during regions of high MUAP overlap, in an easy-to-use software package.

}, doi = {10.64898/2026.01.06.697952}, url = {https://www.biorxiv.org/content/early/2026/01/07/2026.01.06.697952}, author = {O{\textquoteright}Connell, Sean and Michaels, Jonathan A. and Wang, Runming and Mamidipaka, Sahit and Venkatesh, Manikandan and Aresh, Nevin and Pachitariu, Marius and Pruszynski, J. Andrew and Sober, Samuel J. and Pandarinath, Chethan} } @article {70321, title = {High performance sorting of motor unit action potentials with EMUsort}, journal = {eLife}, year = {2026}, month = {2026 Mar 16}, abstract = {

Understanding how neural signals control muscle activity during behavior is a key challenge in motor neuroscience. To this end, recent advances in intramuscular multielectrode arrays have enabled high-quality multichannel recordings of many motor unit action potentials (MUAPs) in freely moving subjects. However, identifying individual MUAP events within multichannel recordings is a significant challenge for existing spike sorting methods, which are typically optimized for identifying action potentials from neurons in the brain. To overcome this challenge, we developed the Enhanced Motor Unit sorter (EMUsort), an extension of Kilosort4 (KS4) that achieves high-performance MUAP spike sorting. We applied EMUsort to high-resolution intramuscular recordings from rat forelimb during locomotion and monkey forelimb during a reaching task. EMUsort improves upon prior methods by addressing key challenges encountered with MUAP datasets, including: 1) long time delays across electrodes due to propagation along muscle fibers, 2) more complex waveform shapes compared to neuronal action potentials, and 3) a high degree of MUAP overlap due to cumulative motor unit recruitment. We compared EMUsort to existing spike sorting methods quantitatively using simulated datasets that closely emulated the rat and monkey datasets we recorded. EMUsort provided median error rate reductions of 67.5\% and 49.9\% during periods of high motor unit activation for the rat and monkey datasets, respectively. In sum, EMUsort provides a substantial improvement to MUAP spike sorter accuracy, especially during regions of high MUAP overlap, in an easy-to-use software package.

}, doi = {10.7554/elife.110417.1}, url = {http://dx.doi.org/10.7554/eLife.110417.1}, author = {O{\textquoteright}Connell, Sean and Michaels, Jonathan A and Wang, Runming and Mamidipaka, Sahit and Venkatesh, Manikandan and Aresh, Nevin and Pachitariu, Marius and Pruszynski, J Andrew and Sober, Samuel J and Pandarinath, Chethan} } @article {70287, title = {Orofacial behaviors, not eye movements, govern neural activity in mouse visual cortex}, journal = {bioRxiv}, year = {2026}, month = {2026 Feb 05}, abstract = {

Neural activity in mouse primary visual cortex (V1) correlates strongly with orofacial movements. Such strong modulation has not been found in the primate visual cortex during eye fixation [1], which led to the suggestion that the modulation may primarily depend on eye movements in both species [2]. Here we examined the influence of eye movements on neural activity in mouse visual cortex both in complete darkness and in the presence of different types of visual input. In all cases, we found that eye movements explain a smaller fraction of neural activity variance compared to orofacial behaviors. Additionally, we found that eye movements were correlated to orofacial movements, such as whisking and sniffing, and thus may be indirectly correlated to neural activity. These results further emphasize the impact of movement signals on mouse visual cortex during free viewing behavior.

}, doi = {10.64898/2026.02.04.703800}, url = {https://www.biorxiv.org/content/early/2026/02/05/2026.02.04.703800}, author = {Syeda, Atika and N{\'u}{\~n}ez-Ochoa, Miguel Angel and Zhong, Lin and Pachitariu, Marius and Stringer, Carsen} } @article {70245, title = {A System for live sorting of neuronal spiking activity from large-scale recordings}, journal = {bioRxiv}, year = {2026}, month = {2026 Jan 02}, abstract = {

Online monitoring and quantification of neural signals has tremendous value both for neurofeedback experiments and for brain-computer interfaces. Unfortunately, established methods of online monitoring primarily involve the use of thresholded neural activity rather than sorted single-neuron spikes. The recent introduction of large-scale, high-density electrophysiology has enabled the recording of activity from hundreds of neurons simultaneously in both model organisms and human participants. This development highlights the need for a robust and easily implementable system for sorting spikes during data collection for {\textquoteleft}live{\textquoteright} analyses of neuronal signals. Here, we describe a system for live sorting of neuronal activity (LSS) based on the widely used Kilosort platform. The LSS workflow utilizes an initial period of recorded neural data to identify waveform templates using Kilosort 4. LSS then interfaces with the SpikeGLX API to retrieve small batches (e.g. 50 ms) of data and for processing online. We measured the similarity of single-neuron activity sorted live by LSS to that sorted offline in neurophysiological recordings from macaque visual cortex using Neuropixels probes. We show that LSS closely replicates the post-stimulus time histograms and visual response tuning curves of single-neurons obtained using offline sorting. Furthermore, we show that decoding neural signals online with LSS consistently outperforms online decoding of thresholded activity, and that LSS can achieve the same performance as that obtained with offline sorting.

}, doi = {10.64898/2025.12.29.696938}, url = {https://www.biorxiv.org/content/early/2026/01/02/2025.12.29.696938}, author = {Muralidharan, Shreyas and Leng, Calvin and Orts, Lucas and Trepka, Ethan and Zhu, Shude and Panichello, Matthew and Jonikaitis, Donatas and Pennington, Jacob and Pachitariu, Marius and Moore, Tirin} } @article {69207, title = {Cellpose3: One-click image restoration for improved cellular segmentation.}, journal = {Nat Methods}, year = {2025}, month = {2025 Feb 12}, abstract = {

Generalist methods for cellular segmentation have good out-of-the-box performance on a variety of image types; however, existing methods struggle for images that are degraded by noise, blurring or undersampling, all of which are common in microscopy. We focused the development of Cellpose3 on addressing these cases and here we demonstrate substantial out-of-the-box gains in segmentation and image quality for noisy, blurry and undersampled images. Unlike previous approaches that train models to restore pixel values, we trained Cellpose3 to output images that are well segmented by a generalist segmentation model, while maintaining perceptual similarity to the target images. Furthermore, we trained the restoration models on a large, varied collection of datasets, thus ensuring good generalization to user images. We provide these tools as {\textquoteright}one-click{\textquoteright} buttons inside the graphical interface of Cellpose as well as in the Cellpose API.

}, doi = {10.1038/s41592-025-02595-5}, url = {https://doi.org/10.1038/s41592-025-02595-5}, author = {Stringer, Carsen and Pachitariu, Marius} } @article {69901, title = {Cellpose-SAM: Superhuman generalization for cellular segmentation}, journal = {bioRxiv}, year = {2025}, month = {2025 May 1}, abstract = {

Modern algorithms for biological segmentation can match inter-human agreement in annotation quality. This however is not a performance bound: a hypothetical human-consensus segmentation could reduce error rates in half. To obtain a model that generalizes better we adapted the pretrained transformer backbone of a foundation model (SAM) to the Cellpose framework. The resulting Cellpose-SAM model substantially outperforms inter-human agreement and approaches the human-consensus bound. We increase generalization performance further by making the model robust to channel shuffling, cell size, shot noise, downsampling, isotropic and anisotropic blur. The new model can be readily adopted into the Cellpose ecosystem which includes finetuning, human-in-the-loop training, image restoration and 3D segmentation approaches. These properties establish Cellpose-SAM as a foundation model for biological segmentation.

}, doi = {10.1101/2025.04.28.651001}, url = {https://www.biorxiv.org/content/early/2025/05/01/2025.04.28.651001}, author = {Pachitariu, Marius and Rariden, Michael and Stringer, Carsen} } @article {69783, title = {Cortical control of innate behavior from subcortical demonstration}, journal = {bioRxiv}, year = {2025}, month = {2025 Feb 13}, abstract = {

Motor control in mammals is traditionally viewed as a hierarchy of descending spinal-targeting pathways, with frontal cortex at the top 1{\textendash}3. Many redundant muscle patterns can solve a given task, and this high dimensionality allows flexibility but poses a problem for efficient learning 4. Although a feasible solution invokes subcortical innate motor patterns, or primitives, to reduce the dimensionality of the control problem, how cortex learns to utilize such primitives remains an open question 5{\textendash}7. To address this, we studied cortical and subcortical interactions as head-fixed mice learned contextual control of innate hindlimb extension behavior. Na{\"\i}ve mice performed reactive extensions to turn off a cold air stimulus within seconds and, using predictive cues, learned to avoid the stimulus altogether in tens of trials. Optogenetic inhibition of large areas of rostral cortex completely prevented avoidance behavior, but did not impair hindlimb extensions in reaction to the cold air stimulus. Remarkably, mice covertly learned to avoid the cold stimulus even without any prior experience of successful, cortically-mediated avoidance. These findings support a dynamic, heterarchical model in which the dominant locus of control can change, on the order of seconds, between cortical and subcortical brain areas. We propose that cortex can leverage periods when subcortex predominates as demonstrations, to learn parameterized control of innate behavioral primitives.

}, doi = {10.1101/2025.02.12.637930}, url = {https://www.biorxiv.org/content/early/2025/02/13/2025.02.12.637930}, author = {Keller, Jason A. and Kwak, Iljung S. and Stark, Alyssa K. and Pachitariu, Marius and Branson, Kristin and Dudman, Joshua T.} } @article {69732, title = {A critical initialization for biological neural networks}, journal = {bioRxiv}, year = {2025}, month = {01/2025}, abstract = {

Artificial neural networks learn faster if they are initialized well. Good initializations can generate high-dimensional macroscopic dynamics with long timescales. It is not known if biological neural networks have similar properties. Here we show that the eigenvalue spectrum and dynamical properties of large-scale neural recordings in mice (two-photon and electrophysiology) are similar to those produced by linear dynamics governed by a random symmetric matrix that is critically normalized. An exception was hippocampal area CA1: population activity in this area resembled an efficient, uncorrelated neural code, which may be optimized for information storage capacity. Global emergent activity modes persisted in simulations with sparse, clustered or spatial connectivity. We hypothesize that the spontaneous neural activity reflects a critical initialization of whole-brain neural circuits that is optimized for learning time-dependent tasks.

}, doi = {10.1101/2025.01.10.632397}, url = {https://www.biorxiv.org/content/early/2025/01/10/2025.01.10.632397}, author = {Pachitariu, Marius and Zhong, Lin and Gracias, Alexa and Minisi, Amanda and Lopez, Crystall and Stringer, Carsen} } @article {70006, title = {Large-scale high-density brain-wide neural recording in nonhuman primates.}, journal = {Nat Neurosci}, year = {2025}, month = {2025 Jun 23}, abstract = {

High-density silicon probes have transformed neuroscience by enabling large-scale neural recordings at single-cell resolution. However, existing technologies have provided limited functionality in nonhuman primates (NHPs) such as macaques. In the present report, we describe the design, fabrication and performance of Neuropixels 1.0 NHP, a high-channel electrode array designed to enable large-scale acute recording throughout large animal brains. The probe features 4,416 recording sites distributed along a 45-mm shank. Experimenters can programmably select 384 recording channels, enabling simultaneous multi-area recording from thousands of neurons with single or multiple probes. This technology substantially increases scalability and recording access relative to existing technologies and enables new classes of experiments that involve electrophysiological mapping of brain areas at single-neuron and single-spike resolution, measurement of spike-spike correlations between cells and simultaneous brain-wide recordings at scale.

Preprint: https://doi.org/10.1101/2023.02.01.526664\ 

}, issn = {1546-1726}, doi = {10.1038/s41593-025-01976-5}, url = {https://doi.org/10.1038/s41593-025-01976-5}, author = {Trautmann, Eric M and Hesse, Janis K and Stine, Gabriel M and Xia, Ruobing and Zhu, Shude and O{\textquoteright}Shea, Daniel J and Karsh, Bill and Colonell, Jennifer and Lanfranchi, Frank F and Vyas, Saurabh and Zimnik, Andrew and Amematsro, Elom and Steinemann, Natalie A and Wagenaar, Daniel A and Pachitariu, Marius and Andrei, Alexandru and Lopez, Carolina Mora and O{\textquoteright}Callaghan, John and Putzeys, Jan and Raducanu, Bogdan C and Welkenhuysen, Marleen and Churchland, Mark and Moore, Tirin and Shadlen, Michael and Shenoy, Krishna and Tsao, Doris and Dutta, Barundeb and Harris, Timothy} } @article {69514, title = {A simplified minimodel of visual cortical neurons}, journal = {Nat Commun}, year = {2025}, month = {2025 Jul 01}, abstract = {

Artificial neural networks (ANNs) have been shown to predict neural responses in primary visual cortex (V1) better than classical models. However, this performance often comes at the expense of simplicity and interpretability. Here we introduce a new class of simplified ANN models that can predict over 70\% of the response variance of V1 neurons. To achieve this high performance, we first recorded a new dataset of over 29,000 neurons responding to up to 65,000 natural image presentations in mouse V1. We found that ANN models required only two convolutional layers for good performance, with a relatively small first layer. We further found that we could make the second layer small without loss of performance, by fitting individual "minimodels" to each neuron. Similar simplifications applied for models of monkey V1 neurons. We show that the minimodels can be used to gain insight into how stimulus invariance arises in biological neurons.

Preprint:\ https://www.biorxiv.org/content/early/2024/07/02/2024.06.30.601394

}, doi = {10.1038/s41467-025-61171-9}, url = {https://doi.org/10.1038/s41467-025-61171-9}, author = {Du, Fengtong and N{\'u}{\~n}ez-Ochoa, Miguel Angel and Pachitariu, Marius and Stringer, Carsen} } @article {70117, title = {Spatial predictive coding in visual cortical neurons}, journal = {bioRxiv}, year = {2025}, month = {2025 Sep 19}, abstract = {

Predictive coding is a theoretical framework that can explain how animals build internal models of their sensory environments by predicting sensory inputs. Predictive coding may capture either spatial or temporal relationships between sensory objects. While the original theory by Rao and Ballard, 1999 described spatial predictive coding, much of the recent experimental data has been interpreted as evidence for temporal predictive coding. Here we directly tested whether the {\textquotedblleft}mismatch{\textquotedblright} neural responses in sensory cortex are due to a spatial or a temporal internal model. We adopted two common paradigms to study predictive coding: one based on virtual-reality and one based on static images. After training mice with repeated visual stimulation for several days, we performed multiple manipulations, including: 1) we introduced a novel stimulus, 2) we replaced a stimulus with a novel gray wall, 3) we duplicated a trained stimulus, or 4) we altered the order of the stimuli. The first two manipulations induced a substantial mismatch response in neural populations of up to 20,000 neurons recorded across primary and higher-order visual cortex, while the third and fourth ones did not. Thus, a mismatch response only occurred if a new spatial {\textendash} not temporal {\textendash} pattern was introduced.

}, doi = {10.1101/2025.09.17.676794}, url = {https://www.biorxiv.org/content/early/2025/09/19/2025.09.17.676794}, author = {Zhang, Qingqing and Gr{\o}dem, Sverre and Gracias, Alexa and Lensj{\o}, Kristian K. and Fyhn, Marianne and Stringer, Carsen and Pachitariu, Marius} } @article {69668, title = {Analysis methods for large-scale neuronal recordings.}, journal = {Science}, volume = {386}, year = {2024}, month = {2024 Nov 08}, pages = {eadp7429}, abstract = {

Simultaneous recordings from hundreds or thousands of neurons are becoming routine because of innovations in instrumentation, molecular tools, and data processing software. Such recordings can be analyzed with data science methods, but it is not immediately clear what methods to use or how to adapt them for neuroscience applications. We review, categorize, and illustrate diverse analysis methods for neural population recordings and describe how these methods have been used to make progress on longstanding questions in neuroscience. We review a variety of approaches, ranging from the mathematically simple to the complex, from exploratory to hypothesis-driven, and from recently developed to more established methods. We also illustrate some of the common statistical pitfalls in analyzing large-scale neural data.

}, keywords = {Action Potentials, Animals, Humans, Models, Neurological, Neurons, Neurosciences, Software}, issn = {1095-9203}, doi = {10.1126/science.adp7429}, author = {Stringer, Carsen and Pachitariu, Marius} } @article {69641, title = {Assembloid model to study loop circuits of the human nervous system}, journal = {bioRxiv}, year = {2024}, month = {2024 Oct 14}, abstract = {

Neural circuits connecting the cerebral cortex, the basal ganglia and the thalamus are fundamental networks for sensorimotor processing and their dysfunction has been consistently implicated in neuropsychiatric disorders1-9. These recursive, loop circuits have been investigated in animal models and by clinical neuroimaging, however, direct functional access to developing human neurons forming these networks has been limited. Here, we use human pluripotent stem cells to reconstruct an in vitro cortico-striatal-thalamic-cortical circuit by creating a four-part loop assembloid. More specifically, we generate regionalized neural organoids that resemble the key elements of the cortico-striatal-thalamic-cortical circuit, and functionally integrate them into loop assembloids using custom 3D-printed biocompatible wells. Volumetric and mesoscale calcium imaging, as well as extracellular recordings from individual parts of these assembloids reveal the emergence of synchronized patterns of neuronal activity. In addition, a multi{\textendash}step rabies retrograde tracing approach demonstrate the formation of neuronal connectivity across the network in loop assembloids. Lastly, we apply this system to study heterozygous loss of ASH1L gene associated with autism spectrum disorder and Tourette syndrome and discover aberrant synchronized activity in disease model assembloids. Taken together, this human multi-cellular platform will facilitate functional investigations of the cortico-striatal-thalamic-cortical circuit in the context of early human development and in disease conditions.

}, doi = {10.1101/2024.10.13.617729}, url = {https://doi.org/10.1101/2024.10.13.617729}, author = {Miura, Yuki and Kim, Ji-il and Jurjut, Ovidiu and Kelley, Kevin W. and Yang, Xiao and Chen, Xiaoyu and Thete, Mayuri Vijay and Revah, Omer and Cui, Bianxiao and Pachitariu, Marius and Pasca, Sergiu P.} } @article {69557, title = {Bridging tuning and invariance with equivariant neuronal representations}, journal = {bioRxiv}, year = {2024}, month = {2024 Aug 02}, abstract = {

As we move through the world, we see the same visual scenes from different perspectives. Although we experience perspective deformations, our perception of a scene remains stable. This raises the question of which neuronal representations in visual brain areas are perspective-tuned and which are invariant. Focusing on planar rotations, we introduce a mathematical framework based on the principle of equivariance, which asserts that an image rotation results in a corresponding rotation of neuronal representations, to explain how the same representation can range from being fully tuned to fully invariant. We applied this framework to large-scale simultaneous neuronal recordings from four visual cortical areas in mice, where we found that representations are both tuned and invariant but become more invariant across higher-order areas. While common deep convolutional neural networks show similar trends in orientation-invariance across layers, they are not rotation-equivariant. We propose that equivariance is a prevalent computation of populations of biological neurons to gradually achieve invariance through structured tuning.

}, doi = {10.1101/2024.08.02.606398}, url = {https://www.doi.org/10.1101/2024.08.02.606398}, author = {Hoeller, Judith and Zhong, Lin and Pachitariu, Marius and Romani, Sandro} } @article {69352, title = {Spike sorting with Kilosort4}, journal = {Nat Methods}, year = {2024}, month = {2024 Apr 08}, 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, made complicated by the nonstationarity of the recordings and the dense overlap in electrical fields between nearby neurons. To address the spike-sorting problem, we have been openly developing the Kilosort framework. Here we describe the various algorithmic steps introduced in different versions of Kilosort. We also report the development of Kilosort4, a version with substantially improved performance due to clustering algorithms inspired by graph-based approaches. To test the performance of Kilosort, we developed a realistic simulation framework that uses densely sampled electrical fields from real experiments to generate nonstationary spike waveforms and realistic noise. We found that nearly all versions of Kilosort outperformed other algorithms on a variety of simulated conditions and that Kilosort4 performed best in all cases, correctly identifying even neurons with low amplitudes and small spatial extents in high drift conditions.

}, issn = {1548-7105}, doi = {10.1038/s41592-024-02232-7}, author = {Pachitariu, Marius and Sridhar, Shashwat and Pennington, Jacob and Stringer, Carsen} } @article {69137, title = {Anatomically distributed neural representations of instincts in the hypothalamus.}, journal = {bioRxiv}, year = {2023}, month = {2023 Dec 07}, abstract = {

Artificial activation of anatomically localized, genetically defined hypothalamic neuron populations is known to trigger distinct innate behaviors, suggesting a hypothalamic nucleus-centered organization of behavior control. To assess whether the encoding of behavior is similarly anatomically confined, we performed simultaneous neuron recordings across twenty hypothalamic regions in freely moving animals. Here we show that distinct but anatomically distributed neuron ensembles encode the social and fear behavior classes, primarily through mixed selectivity. While behavior class-encoding ensembles were spatially distributed, individual ensembles exhibited strong localization bias. Encoding models identified that behavior actions, but not motion-related variables, explained a large fraction of hypothalamic neuron activity variance. These results identify unexpected complexity in the hypothalamic encoding of instincts and provide a foundation for understanding the role of distributed neural representations in the expression of behaviors driven by hardwired circuits.

}, doi = {10.1101/2023.11.21.568163}, url = {https://doi.org/10.1101/2023.11.21.568163}, author = {Stagkourakis, Stefanos and Spigolon, Giada and Marks, Markus and Feyder, Michael and Kim, Joseph and Perona, Pietro and Pachitariu, Marius and Anderson, David J} } @article {68464, title = {Cellpose 2.0: how to train your own model.}, journal = {Nature Methods}, volume = {19}, year = {2022}, month = {2022 Nov 07}, pages = {1634-41}, abstract = {

Pretrained neural network models for biological segmentation can provide good out-of-the-box results for many image types. However, such models do not allow users to adapt the segmentation style to their specific needs and can perform suboptimally for test images that are very different from the training images. Here we introduce Cellpose 2.0, a new package that includes an ensemble of diverse pretrained models as well as a human-in-the-loop pipeline for rapid prototyping of new custom models. We show that models pretrained on the Cellpose dataset can be fine-tuned with only 500-1,000 user-annotated regions of interest (ROI) to perform nearly as well as models trained on entire datasets with up to 200,000 ROI. A human-in-the-loop approach further reduced the required user annotation to 100-200 ROI, while maintaining high-quality segmentations. We provide software tools such as an annotation graphical user interface, a model zoo and a human-in-the-loop pipeline to facilitate the adoption of Cellpose 2.0.

}, issn = {1548-7105}, doi = {10.1038/s41592-022-01663-4}, author = {Pachitariu, Marius and Stringer, Carsen} } @article {68386, title = {Structured random receptive fields enable informative sensory encodings.}, journal = {PLoS Computational Biology}, volume = {18}, year = {2022}, month = {2022 Oct 10}, pages = {e1010484}, abstract = {

Brains must represent the outside world so that animals survive and thrive. In early sensory systems, neural populations have diverse receptive fields structured to detect important features in inputs, yet significant variability has been ignored in classical models of sensory neurons. We model neuronal receptive fields as random, variable samples from parameterized distributions and demonstrate this model in two sensory modalities using data from insect mechanosensors and mammalian primary visual cortex. Our approach leads to a significant theoretical connection between the foundational concepts of receptive fields and random features, a leading theory for understanding artificial neural networks. The modeled neurons perform a randomized wavelet transform on inputs, which removes high frequency noise and boosts the signal. Further, these random feature neurons enable learning from fewer training samples and with smaller networks in artificial tasks. This structured random model of receptive fields provides a unifying, mathematically tractable framework to understand sensory encodings across both spatial and temporal domains.

}, issn = {1553-7358}, doi = {10.1371/journal.pcbi.1010484}, author = {Pandey, Biraj and Pachitariu, Marius and Brunton, Bingni W and Harris, Kameron Decker} } @article {67859, title = {Toroidal topology of population activity in grid cells.}, journal = {Nature}, volume = {602}, year = {2022}, month = {2022 Jan 12}, pages = {123-128}, abstract = {

The medial entorhinal cortex is part of a neural system for mapping the position of an individual within a physical environment. Grid cells, a key component of this system, fire in a characteristic hexagonal pattern of locations, and are organized in modules that collectively form a population code for the animal{\textquoteright}s allocentric position. The invariance of the correlation structure of this population code across environments and behavioural states, independent of specific sensory inputs, has pointed to intrinsic, recurrently connected continuous attractor networks (CANs) as a possible substrate of the grid pattern. However, whether grid cell networks show continuous attractor dynamics, and how they interface with inputs from the environment, has remained unclear owing to the small samples of cells obtained so far. Here, using simultaneous recordings from many hundreds of grid cells and subsequent topological data analysis, we show that the joint activity of grid cells from an individual module resides on a toroidal manifold, as expected in a two-dimensional CAN. Positions on the torus correspond to positions of the moving animal in the environment. Individual cells are preferentially active at singular positions on the torus. Their positions are maintained between environments and from wakefulness to sleep, as predicted by CAN models for grid cells but not by alternative feedforward models. This demonstration of network dynamics on a toroidal manifold provides a population-level visualization of CAN dynamics in grid cells.

}, issn = {1476-4687}, doi = {10.1038/s41586-021-04268-7}, author = {Gardner, Richard J and Hermansen, Erik and Pachitariu, Marius and Burak, Yoram and Baas, Nils A and Dunn, Benjamin A and Moser, May-Britt and Moser, Edvard I} } @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 {67721, title = {Electrode pooling can boost the yield of extracellular recordings with switchable silicon probes.}, journal = {Nature Communications}, volume = {12}, year = {2021}, month = {2021 Sep 02}, pages = {5245}, abstract = {

State-of-the-art silicon probes for electrical recording from neurons have thousands of recording sites. However, due to volume limitations there are typically many fewer wires carrying signals off the probe, which restricts the number of channels that can be recorded simultaneously. To overcome this fundamental constraint, we propose a method called electrode pooling that uses a single wire to serve many recording sites through a set of controllable switches. Here we present the framework behind this method and an experimental strategy to support it. We then demonstrate its feasibility by implementing electrode pooling on the Neuropixels 1.0 electrode array and characterizing its effect on signal and noise. Finally we use simulations to explore the conditions under which electrode pooling saves wires without compromising the content of the recordings. We make recommendations on the design of future devices to take advantage of this strategy.

}, keywords = {Action Potentials, Animals, Brain, Electrodes, Implanted, Electrophysiology, Equipment Design, Extracellular Space, Mice, Nerve Net, Neurons, Signal Processing, Computer-Assisted, Silicon}, issn = {2041-1723}, doi = {10.1038/s41467-021-25443-4}, author = {Lee, Kyu Hyun and Ni, Yu-Li and Colonell, Jennifer and Karsh, Bill and Putzeys, Jan and Pachitariu, Marius and Harris, Timothy D and Meister, Markus} } @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 {67263, title = {Hunger or thirst state uncertainty is resolved by outcome evaluation in medial prefrontal cortex to guide decision-making.}, journal = {Nature Neuroscience}, volume = {24}, year = {2021}, month = {2021 Jul 01}, pages = {907-912}, abstract = {

Physiological need states direct decision-making toward re-establishing homeostasis. Using a two-alternative forced choice task for mice that models elements of human decisions, we found that varying hunger and thirst states caused need-inappropriate choices, such as food seeking when thirsty. These results show limits on interoceptive knowledge of hunger and thirst states to guide decision-making. Instead, need states were identified after food and water consumption by outcome evaluation, which depended on the medial prefrontal cortex.

}, issn = {1546-1726}, doi = {10.1038/s41593-021-00850-4}, author = {Eiselt, Anne-Kathrin and Chen, Susu and Chen, Jim and Arnold, Jon and Kim, Tahnbee and Pachitariu, Marius and Sternson, Scott M} } @article {67232, title = {Neuropixels 2.0: A miniaturized high-density probe for stable, long-term brain recordings.}, journal = {Science}, volume = {372}, year = {2021}, month = {2021 Apr 16}, abstract = {

Measuring the dynamics of neural processing across time scales requires following the spiking of thousands of individual neurons over milliseconds and months. To address this need, we introduce the Neuropixels 2.0 probe together with newly designed analysis algorithms. The probe has more than 5000 sites and is miniaturized to facilitate chronic implants in small mammals and recording during unrestrained behavior. High-quality recordings over long time scales were reliably obtained in mice and rats in six laboratories. Improved site density and arrangement combined with newly created data processing methods enable automatic post hoc correction for brain movements, allowing recording from the same neurons for more than 2 months. These probes and algorithms enable stable recordings from thousands of sites during free behavior, even in small animals such as mice.

}, issn = {1095-9203}, doi = {10.1126/science.abf4588}, author = {Steinmetz, Nicholas A and Ayd{\i}n, {\c C}a{\u g}atay and Lebedeva, Anna and Okun, Michael and Pachitariu, Marius and Bauza, Marius and Beau, Maxime and Bhagat, Jai and B{\"o}hm, Claudia and Broux, Martijn and Chen, Susu and Colonell, Jennifer and Gardner, Richard J and Karsh, Bill and Kloosterman, Fabian and Kostadinov, Dimitar and Mora-Lopez, Carolina and O{\textquoteright}Callaghan, John and Park, Junchol and Putzeys, Jan and Sauerbrei, Britton and van Daal, Rik J J and Vollan, Abraham Z and Wang, Shiwei and Welkenhuysen, Marleen and Ye, Zhiwen and Dudman, Joshua T and Dutta, Barundeb and Hantman, Adam W and Harris, Kenneth D and Lee, Albert K and Moser, Edvard I and O{\textquoteright}Keefe, John and Renart, Alfonso and Svoboda, Karel and H{\"a}usser, Michael and Haesler, Sebastian and Carandini, Matteo and Harris, Timothy D} } @article {66008, title = {Survey of spiking in the mouse visual system reveals functional hierarchy.}, journal = {Nature}, volume = {592(7852):86-92}, year = {2021}, month = {2021 Jan 20}, pages = {86-92}, abstract = {

The anatomy of the mammalian visual system, from the retina to the neocortex, is organized hierarchically. However, direct observation of cellular-level functional interactions across this hierarchy is lacking due to the challenge of simultaneously recording activity across numerous regions. Here we describe a large, open dataset-part of the Allen Brain Observatory-that surveys spiking from tens of thousands of units in six cortical and two thalamic regions in the brains of mice responding to a battery of visual stimuli. Using cross-correlation analysis, we reveal that the organization of inter-area functional connectivity during visual stimulation mirrors the anatomical hierarchy from the Allen Mouse Brain Connectivity Atlas. We find that four classical hierarchical measures-response latency, receptive-field size, phase-locking to drifting gratings and response decay timescale-are all correlated with the hierarchy. Moreover, recordings obtained during a visual task reveal that the correlation between neural activity and behavioural choice also increases along the hierarchy. Our study provides a foundation for understanding coding and signal propagation across hierarchically organized cortical and thalamic visual areas.

}, issn = {1476-4687}, doi = {10.1038/s41586-020-03171-x}, author = {Siegle, Joshua H and Jia, Xiaoxuan and Durand, Severine and Gale, Sam and Bennett, Corbett and Graddis, Nile and Heller, Greggory and Ramirez, Tamina K and Choi, Hannah and Luviano, Jennifer A and Groblewski, Peter A and Ahmed, Ruweida and Arkhipov, Anton and Bernard, Amy and Billeh, Yazan N and Brown, Dillan and Buice, Michael A and Cain, Nicolas and Caldejon, Shiella and Casal, Linzy and Cho, Andrew and Chvilicek, Maggie and Cox, Timothy C and Dai, Kael and Denman, Daniel J and de Vries, Saskia E J and Dietzman, Roald and Esposito, Luke and Farrell, Colin and Feng, David and Galbraith, John and Garrett, Marina and Gelfand, Emily C and Hancock, Nicole and Harris, Julie A and Howard, Robert and Hu, Brian and Hytnen, Ross and Iyer, Ramakrishnan and Jessett, Erika and Johnson, Katelyn and Kato, India and Kiggins, Justin and Lambert, Sophie and Lecoq, Jerome and Ledochowitsch, Peter and Lee, Jung Hoon and Leon, Arielle and Li, Yang and Liang, Elizabeth and Long, Fuhui and Mace, Kyla and Melchior, Jose and Millman, Daniel and Mollenkopf, Tyler and Nayan, Chelsea and Ng, Lydia and Ngo, Kiet and Nguyen, Thuyahn and Nicovich, Philip R and North, Kat and Ocker, Gabriel Koch and Ollerenshaw, Doug and Oliver, Michael and Pachitariu, Marius and Perkins, Jed and Reding, Melissa and Reid, David and Robertson, Miranda and Ronellenfitch, Kara and Seid, Sam and Slaughterbeck, Cliff and Stoecklin, Michelle and Sullivan, David and Sutton, Ben and Swapp, Jackie and Thompson, Carol and Turner, Kristen and Wakeman, Wayne and Whitesell, Jennifer D and Williams, Derric and Williford, Ali and Young, Rob and Zeng, Hongkui and Naylor, Sarah and Phillips, John W and Reid, R Clay and Mihalas, Stefan and Olsen, Shawn R and Koch, Christof} } @article {65615, title = {Arousal modulates retinal output.}, journal = {Neuron}, volume = {107}, year = {2020}, month = {2020 Aug 01}, pages = {487}, abstract = {

At various stages of the visual system, visual responses are modulated by arousal. Here, we find that in mice this modulation operates as early as in the first synapse from the retina and even in retinal axons. To measure retinal activity in the awake, intact brain, we imaged the synaptic boutons of retinal axons in the superior colliculus. Their activity depended not only on vision but also on running speed and pupil size, regardless of retinal illumination. Arousal typically reduced their visual responses and selectivity for direction and orientation. Recordings from retinal axons in the optic tract revealed that arousal modulates the firing of some retinal ganglion cells. Arousal had similar effects postsynaptically in colliculus neurons, independent of activity in the other main source of visual inputs to the colliculus, the primary visual cortex. These results indicate that arousal modulates activity at every stage of the mouse visual system.

}, issn = {1097-4199}, doi = {10.1016/j.neuron.2020.04.026}, author = {Schr{\"o}der, Sylvia and Steinmetz, Nicholas A and Krumin, Michael and Pachitariu, Marius and Rizzi, Matteo and Lagnado, Leon and Harris, Kenneth D and Carandini, Matteo} } @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} } @article {49534, title = {High-dimensional geometry of population responses in visual cortex.}, journal = {Nature}, volume = {571}, year = {2019}, month = {2019 Jun 26}, pages = {361-65}, abstract = {

A neuronal population encodes information most efficiently when its activity is uncorrelated and high-dimensional, and most robustly when its activity is correlated and lower-dimensional. Here, we analyzed the correlation structure of natural image coding, in 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 was not inherited from the 1/f spectrum of natural images, because it persisted after stimulus whitening. We proved mathematically that the variance spectrum must decay at least this fast if a population code is smooth, i.e. if small changes in input cannot 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 represents a fundamental constraint governing correlations in neural population codes.

}, doi = {10.1038/s41586-019-1346-5}, author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas A and Carandini, Matteo and Harris, Kenneth D} } @article {49187, title = {Spontaneous behaviors drive multidimensional, brain-wide population activity.}, journal = {Science}, volume = {364}, year = {2019}, month = {2019 Apr 18}, pages = {255}, abstract = {

Sensory cortices are active in the absence of external sensory stimuli. To understand the nature of this ongoing activity, we used two-photon calcium imaging to record from over 10,000 neurons in the visual cortex of mice awake in darkness while monitoring their behavior videographically. Ongoing population activity was multidimensional, exhibiting at least 100 significant dimensions, some of which were related to the spontaneous behaviors of the mice. The largest single dimension was correlated with the running speed and pupil area, while a 16-dimensional summary of orofacial behaviors could predict ~45\% of the explainable neural variance. Electrophysiological recordings with 8 simultaneous Neuropixels probes revealed a similar encoding of high-dimensional orofacial behaviors across multiple forebrain regions. Representation of motor variables continued uninterrupted during visual stimulus presentation, occupying dimensions nearly orthogonal to the stimulus responses. Our results show that a multidimensional representation of motor state is encoded across the forebrain, and is integrated with visual input by neuronal populations in primary visual cortex.

}, doi = {10.1101/306019}, author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas A and Reddy, Charu Bai and Carandini, Matteo and Harris, Kenneth D} } @article {64871, title = {Thirst regulates motivated behavior through modulation of brainwide neural population dynamics.}, journal = {Science}, volume = {364}, year = {2019}, month = {2019 Apr 04}, pages = {253}, abstract = {

Physiological needs produce motivational drives, such as thirst and hunger, that regulate behaviors essential to survival. Hypothalamic neurons sense these needs and must coordinate relevant brainwide neuronal activity to produce the appropriate behavior. We studied dynamics from ~24,000 neurons in 34 brain regions during thirst-motivated choice behavior, as mice consumed water and became sated. Water-predicting sensory cues elicited activity that rapidly spread throughout the brain of thirsty animals. These dynamics were gated by a brainwide mode of population activity that encoded motivational state. Focal optogenetic activation of hypothalamic thirst-sensing neurons, after satiation, returned global activity to the pre-satiation state. Thus, motivational states specify initial conditions determining how a brainwide dynamical system transforms sensory input into behavioral output.

}, issn = {1095-9203}, doi = {10.1126/science.aav3932}, author = {Allen, William E and Chen, Michael Z and Pichamoorthy, Nandini and Tien, Rebecca H and Pachitariu, Marius and Luo, Liqun and Deisseroth, Karl} } @article {67219, title = {Community-based benchmarking improves spike rate inference from two-photon calcium imaging data}, journal = {PLOS Computational Biology}, volume = {14}, year = {2018}, month = {Sep-05-2019}, pages = {e1006157}, abstract = {

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the\ spikefinder\ challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

}, doi = {10.1371/journal.pcbi.1006157}, url = {https://journals.plos.org/ploscompbiol/article?rev=2\&id=10.1371/journal.pcbi.1006157}, author = {Berens, Philipp and Freeman, Jeremy and Deneux, Thomas and Chenkov, Nikolay and McColgan, Thomas and Speiser, Artur and Macke, Jakob H. and Turaga, Srinivas C. and Mineault, Patrick and Rupprecht, Peter and Gerhard, Stephan and Friedrich, Rainer W. and Friedrich, Johannes and Paninski, Liam and Pachitariu, Marius and Harris, Kenneth D. and Bolte, Ben and Machado, Timothy A. and Ringach, Dario and Stone, Jasmine and Rogerson, Luke E. and Sofroniew, Nicolas J. and Reimer, Jacob and Froudarakis, Emmanouil and Euler, Thomas and Rom{\'a}n Ros{\'o}n, Miroslav and Theis, Lucas and Tolias, Andreas S. and Bethge, Matthias}, editor = {Bush, Daniel} } @article {49580, title = {Computational processing of neural recordings from calcium imaging data.}, journal = {Current Opinion in Neurobiology}, volume = {55}, year = {2018}, month = {2019 Apr }, pages = {22-31}, abstract = {

Electrophysiology has long been the workhorse of neuroscience, allowing scientists to record with millisecond precision the action potentials generated by neurons in vivo. Recently, calcium imaging of fluorescent indicators has emerged as a powerful alternative. This technique has its own strengths and weaknesses and unique data processing problems and interpretation confounds. Here we review the computational methods that convert raw calcium movies to estimates of single neuron spike times with minimal human supervision. By computationally addressing the weaknesses of calcium imaging, these methods hold the promise of significantly improving data quality. We also introduce a new metric to evaluate the output of these processing pipelines, which is based on the cluster isolation distance routinely used in electrophysiology.

}, issn = {1873-6882}, doi = {10.1016/j.conb.2018.11.005}, author = {Stringer, Carsen and Pachitariu, Marius} } @article {49345, title = {Robustness of spike deconvolution for neuronal calcium imaging.}, journal = {The Journal of Neuroscience : the official journal of the Society for Neuroscience}, volume = {38}, year = {2018}, month = {2018 Aug 06}, pages = {7976-85}, abstract = {

Calcium imaging is a powerful method to record the activity of neural populations in many species, but inferring spike times from calcium signals is a challenging problem. We compared multiple approaches using multiple datasets with ground truth electrophysiology, and found that simple non-negative deconvolution (NND) outperformed all other algorithms on out-of-sample test data. We introduce a novel benchmark applicable to recordings without electrophysiological ground truth, based on the correlation of responses to two stimulus repeats, and used this to show that unconstrained NND also outperformed the other algorithms when run on "zoomed out" datasets of \~{}10,000 cell recordings from the visual cortex of mice of either sex. Finally, we show that NND-based methods match the performance of a supervised method based on convolutional neural networks, while avoiding some of the biases of such methods, and at much faster running times. We therefore recommend that spikes be inferred from calcium traces using simple NND, due to its simplicity, efficiency and accuracy.The experimental method that currently allows for recordings of the largest numbers of cells simultaneously is two-photon calcium imaging. However, use of this powerful method requires that neuronal firing times be inferred correctly from the large resulting datasets. Previous studies have claimed that complex supervised learning algorithms outperform simple deconvolution methods at this task. Unfortunately, these studies suffered from several problems and biases. When we repeated the analysis, using the same data and correcting these problems, we found that simpler spike inference methods perform better. Even more importantly, we found that supervised learning methods can introduce artifactual structure into spike trains, that can in turn lead to erroneous scientific conclusions. Of the algorithms we evaluated, we found that an extremely simple method performed best in all circumstances tested, was much faster to run, and was insensitive to parameter choices, making incorrect scientific conclusions much less likely.

}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.3339-17.2018}, author = {Pachitariu, Marius and Stringer, Carsen and Harris, Kenneth D} } @article {48839, title = {Vision and locomotion shape the interactions between neuron types in mouse visual cortex.}, journal = {Neuron}, volume = {98}, year = {2018}, month = {2018 May 2}, pages = {602-15}, abstract = {

Cortical computation arises from the interaction of multiple neuronal\ types, including pyramidal (Pyr) cells and interneurons expressing Sst, Vip, or Pvalb. To study the circuit underlying such\ interactions, we imaged these four\ types\ of cells in\ mouse\ primary\ visual\ cortex(V1). Our recordings in darkness were consistent with a "disinhibitory" model in which\ locomotion\ activates Vip cells, thus inhibiting Sst cells and disinhibiting Pyr cells. However, the disinhibitory model failed when\ visual\ stimuli were present:\ locomotion\ increased Sst cell responses to large stimuli and Vip cell responses to small stimuli. A recurrent network model successfully predicted each cell type{\textquoteright}s activity from the measured activity of other\ types. Capturing the effects of\ locomotion, however, required allowing it to increase feedforward synaptic weights and modulate recurrent weights. This network model summarizes interneuron\ interactions\ and suggests that\ locomotion\ may alter cortical computation by changing effective synaptic connectivity.

}, doi = {https://doi.org/10.1101/058396}, author = {Dipoppa, Mario and Ranson, Adam and Krumin, Michael and Pachitariu, Marius and Carandini, Matteo and Harris, Kenneth D} } @article {48836, title = {Community-based benchmarking improves spike inference from two-photon calcium imaging data.}, journal = {PLoS Computational Biology}, volume = {14}, year = {2017}, month = {2018 May 21}, pages = {e1006157}, abstract = {

In recent years, two-photon calcium imaging has become a standard tool to probe the function of neural circuits and to study computations in neuronal populations. However, the acquired signal is only an indirect measurement of neural activity due to the comparatively slow dynamics of fluorescent calcium indicators. Different algorithms for estimating spike rates from noisy calcium measurements have been proposed in the past, but it is an open question how far performance can be improved. Here, we report the results of the spikefinder challenge, launched to catalyze the development of new spike rate inference algorithms through crowd-sourcing. We present ten of the submitted algorithms which show improved performance compared to previously evaluated methods. Interestingly, the top-performing algorithms are based on a wide range of principles from deep neural networks to generative models, yet provide highly correlated estimates of the neural activity. The competition shows that benchmark challenges can drive algorithmic developments in neuroscience.

}, doi = {10.1371/journal.pcbi.1006157}, author = {Berens, Philipp and Freeman, Jeremy and Deneux, Thomas and Chenkov, Nicolay and McColgan, Thomas and Speiser, Artur and Macke, Jakob H and Turaga, Srinivas C and Mineault, Patrick and Rupprecht, Peter and Gerhard, Stephan and Friedrich, Rainer W and Friedrich, Johannes and Paninski, Liam and Pachitariu, Marius and Harris, Kenneth D and Bolte, Ben and Machado, Timothy A and Ringach, Dario and etal} } @article {48935, title = {Fully integrated silicon probes for high-density recording of neural activity.}, journal = {Nature}, volume = {551}, year = {2017}, month = {2017 Nov 08}, pages = {232-236}, abstract = {

Sensory, motor and cognitive operations involve the coordinated action of large neuronal populations across multiple brain regions in both superficial and deep structures. Existing extracellular probes record neural activity with excellent spatial and temporal (sub-millisecond) resolution, but from only a few dozen neurons per shank. Optical Ca(2+) imaging offers more coverage but lacks the temporal resolution needed to distinguish individual spikes reliably and does not measure local field potentials. Until now, no technology compatible with use in unrestrained animals has combined high spatiotemporal resolution with large volume coverage. Here we design, fabricate and test a new silicon probe known as Neuropixels to meet this need. Each probe has 384 recording channels that can programmably address 960 complementary metal-oxide-semiconductor (CMOS) processing-compatible low-impedance TiN sites that tile a single 10-mm long, 70 {\texttimes} 20-μm cross-section shank. The 6 {\texttimes} 9-mm probe base is fabricated with the shank on a single chip. Voltage signals are filtered, amplified, multiplexed and digitized on the base, allowing the direct transmission of noise-free digital data 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 recorded simultaneously from five brain structures in an awake mouse. The fully integrated functionality and small size of Neuropixels probes allowed large populations of neurons from several brain structures to be recorded in freely moving animals. This combination of high-performance electrode technology and scalable chip fabrication methods opens a path towards recording of brain-wide neural activity during behaviour.

}, issn = {1476-4687}, doi = {10.1038/nature24636}, author = {Jun, James J and Steinmetz, Nicholas A and Siegle, Joshua H and Denman, Daniel J and Bauza, Marius and Barbarits, Brian and Lee, Albert K and Anastassiou, Costas A and Andrei, Alexandru and Ayd{\i}n, {\c C}a{\u g}atay and Barbic, Mladen and Blanche, Timothy J and Bonin, Vincent and Couto, Jo{\~a}o and Dutta, Barundeb and Gratiy, Sergey L and Gutnisky, Diego A and H{\"a}usser, Michael and Karsh, Bill and Ledochowitsch, Peter and Lopez, Carolina Mora and Mitelut, Catalin and Musa, Silke and Okun, Michael and Pachitariu, Marius and Putzeys, Jan and Rich, P Dylan and Rossant, Cyrille and Sun, Wei-Lung and Svoboda, Karel and Carandini, Matteo and Harris, Kenneth D and Koch, Christof and O{\textquoteright}Keefe, John and Harris, Timothy D} } @article {48837, title = {Robustness of spike deconvolution for calcium imaging of neural spiking.}, journal = {bioRxiv}, year = {2017}, month = {2017 Jun 27}, pages = {156786}, abstract = {

Calcium imaging is a powerful method to record the activity of neural populations, but inferring spike times from calcium signals is a challenging problem. We compared multiple approaches using multiple datasets with ground truth electrophysiology, and found that simple non-negative deconvolution (NND) outperformed all other algorithms. We introduce a novel benchmark applicable to recordings without electrophysiological ground truth, based on the correlation of responses to two stimulus repeats, and used this to show that unconstrained NND also outperformed the other algorithms when run on {\textquoteright}zoomed out{\textquoteright} datasets of ~10,000 cell recordings. Finally, we show that NND-based methods match the performance of a supervised method based on convolutional neural networks, while avoiding some of the biases of such methods, and at much faster running times. We therefore recommend that spikes be inferred from calcium traces using simple NND, due to its simplicity, efficiency and accuracy.

}, doi = {https://doi.org/10.1101/156786}, author = {Pachitariu, Marius and Stringer, Carsen and Harris, Kenneth D} } @article {48840, title = {Suite2p: beyond 10,000 neurons with standard two-photon microscopy.}, journal = {bioRxiv}, year = {2017}, month = {2017 Jul 20}, pages = {061507}, abstract = {

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 two-photon 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.

}, doi = {https://doi.org/10.1101/061507}, author = {Pachitariu, Marius and Stringer, Carsen and Dipoppa, Mario and Schr{\"o}der, Sylvia and Rossi, L Federico and Dalgleish, Henry and Carandini, Matteo and Harris, Kenneth D} } @article {48838, title = {Visual motion computation in recurrent neural networks.}, journal = {bioRxiv}, year = {2017}, month = {2017 Jan 09}, pages = {099101}, abstract = {

Populations of neurons in primary visual cortex (V1) transform direct thalamic inputs into a cortical representation which acquires new spatio-temporal properties. One of these properties, motion selectivity, has not been strongly tied to putative neural mechanisms, and its origins remain poorly understood. Here we propose that motion selectivity is acquired through the recurrent mechanisms of a network of strongly connected neurons. We first show that a bank of V1 spatiotemporal receptive fields can be generated accurately by a network which receives only instantaneous inputs from the retina. The temporal structure of the receptive fields is generated by the long timescale dynamics associated with the high magnitude eigenvalues of the recurrent connectivity matrix. When these eigenvalues have complex parts, they generate receptive fields that are inseparable in time and space, such as those tuned to motion direction. We also show that the recurrent connectivity patterns can be learnt directly from the statistics of natural movies using a temporally-asymmetric Hebbian learning rule. Probed with drifting grating stimuli and moving bars, neurons in the model show patterns of responses analogous to those of direction-selective simple cells in primary visual cortex. These computations are enabled by a specific pattern of recurrent connections, that can be tested by combining connectome reconstructions with functional recordings.

}, doi = {https://doi.org/10.1101/099101}, author = {Pachitariu, Marius and Sahani, Maneesh} } @conference {48832, title = {Fast and accurate spike sorting of high-channel count probes with KiloSort.}, booktitle = {Neural Information Processing Systems (NIPS 2016)}, year = {2016}, month = {2016 Dec 05}, address = {Barcelona, Spain}, abstract = {

New silicon technology is enabling large-scale electrophysiological recordings in vivo from hundreds to thousands of channels. Interpreting these recordings requires scalable and accurate automated methods for spike sorting, which should minimize the time required for manual curation of the results. Here we introduce KiloSort, a new integrated spike sorting framework that uses template matching both during spike detection and during spike clustering. KiloSort models the electrical voltage as a sum of template waveforms triggered on the spike times, which allows overlapping spikes to be identified and resolved. Unlike previous algorithms that compress the data with PCA, KiloSort operates on the raw data which allows it to construct a more accurate model of the waveforms. Processing times are faster than in previous algorithms thanks to batch-based optimization on GPUs. We compare KiloSort to an established algorithm and show favorable performance, at much reduced processing times. A novel post-clustering merging step based on the continuity of the templates further reduced substantially the number of manual operations required on this data, for the neurons with near-zero error rates, paving the way for fully automated spike sorting of multichannel electrode recordings.

}, url = {http://papers.nips.cc/paper/6326-fast-and-accurate-spike-sorting-of-high-channel-count-probes-with-kilosort}, author = {Pachitariu, Marius and Steinmetz, Nicholas A and Kadir, Shabnam N and Carandini, Matteo and Harris, Kenneth D} } @article {48842, title = {Inhibitory control of correlated intrinsic variability in cortical networks.}, journal = {eLife}, volume = {5}, year = {2016}, month = {2016 Dec 07}, abstract = {

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

}, issn = {2050-084X}, doi = {10.7554/eLife.19695}, author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas A and Okun, Michael and Bartho, Peter and Harris, Kenneth D and Sahani, Maneesh and Lesica, Nicholas A} } @article {65751, title = {Inhibitory control of correlated intrinsic variability in cortical networks}, journal = {Elife}, volume = {5}, year = {2016}, month = {12/2016}, pages = {e19695}, abstract = {

Cortical networks exhibit intrinsic dynamics that drive coordinated, large-scale fluctuations across neuronal populations and create noise correlations that impact sensory coding. To investigate the network-level mechanisms that underlie these dynamics, we developed novel computational techniques to fit a deterministic spiking network model directly to multi-neuron recordings from different rodent species, sensory modalities, and behavioral states. The model generated correlated variability without external noise and accurately reproduced the diverse activity patterns in our recordings. Analysis of the model parameters suggested that differences in noise correlations across recordings were due primarily to differences in the strength of feedback inhibition. Further analysis of our recordings confirmed that putative inhibitory neurons were indeed more active during desynchronized cortical states with weak noise correlations. Our results demonstrate that network models with intrinsically-generated variability can accurately reproduce the activity patterns observed in multi-neuron recordings and suggest that inhibition modulates the interactions between intrinsic dynamics and sensory inputs to control the strength of noise correlations.

}, doi = {https://doi.org/10.7554/eLife.19695}, author = {Stringer, Carsen and Pachitariu, Marius and Steinmetz, Nicholas A and Okun, Michael and Bartho, Peter and Harris, Kenneth D and Sahani, Maneesh and Lesica, Nicholas A} } @article {48830, title = {Learning enhances sensory and multiple non-sensory representations in primary visual cortex.}, journal = {Neuron}, volume = {86}, year = {2015}, month = {2015 Jun 17}, pages = {1478-90}, abstract = {

We determined how learning modifies neural representations in primary visual cortex (V1) during acquisition of a visually guided behavioral task. We imaged the activity of the same layer 2/3 neuronal populations as mice learned to discriminate two visual patterns while running through a virtual corridor, where one pattern was rewarded. Improvements in behavioral performance were closely associated with increasingly distinguishable population-level representations of task-relevant stimuli, as a result of stabilization of existing and recruitment of new neurons selective for these stimuli. These effects correlated with the appearance of multiple task-dependent signals during learning: those that increased neuronal selectivity across the population when expert animals engaged in the task, and those reflecting anticipation or behavioral choices specifically in neuronal subsets preferring the rewarded stimulus. Therefore, learning engages diverse mechanisms that modify sensory and non-sensory representations in V1 to adjust its processing to task requirements and the behavioral relevance of visual stimuli.

}, keywords = {Animals, Calcium, Discrimination (Psychology), Female, Learning, Luminescent Proteins, Male, Mice, Mice, Inbred C57BL, Mice, Transgenic, Models, Neurological, Neurons, Nonlinear Dynamics, Optogenetics, Photic Stimulation, Sensory Receptor Cells, User-Computer Interface, Vision, Ocular, Visual Cortex}, issn = {1097-4199}, doi = {10.1016/j.neuron.2015.05.037}, author = {Poort, Jasper and Khan, Adil G and Pachitariu, Marius and Nemri, Abdellatif and Orsolic, Ivana and Krupic, Julija and Bauza, Marius and Sahani, Maneesh and Keller, Georg B and Mrsic-Flogel, Thomas D and Hofer, Sonja B} } @article {48831, title = {State-dependent population coding in primary auditory cortex.}, journal = {The Journal of Neuroscience : the official journal of the Society for Neuroscience}, volume = {35}, year = {2015}, month = {2015 Feb 04}, pages = {2058-73}, abstract = {

Sensory function is mediated by interactions between external stimuli and intrinsic cortical dynamics that are evident in the modulation of evoked responses by cortical state. A number of recent studies across different modalities have demonstrated that the patterns of activity in neuronal populations can vary strongly between synchronized and desynchronized cortical states, i.e., in the presence or absence of intrinsically generated up and down states. Here we investigated the impact of cortical state on the population coding of tones and speech in the primary auditory cortex (A1) of gerbils, and found that responses were qualitatively different in synchronized and desynchronized cortical states. Activity in synchronized A1 was only weakly modulated by sensory input, and the spike patterns evoked by tones and speech were unreliable and constrained to a small range of patterns. In contrast, responses to tones and speech in desynchronized A1 were temporally precise and reliable across trials, and different speech tokens evoked diverse spike patterns with extremely weak noise correlations, allowing responses to be decoded with nearly perfect accuracy. Restricting the analysis of synchronized A1 to activity within up states yielded similar results, suggesting that up states are not equivalent to brief periods of desynchronization. These findings demonstrate that the representational capacity of A1 depends strongly on cortical state, and suggest that cortical state should be considered as an explicit variable in all studies of sensory processing.

}, keywords = {Animals, Auditory Cortex, Cortical Synchronization, Evoked Potentials, Auditory, Gerbillinae, Male, Neurons}, issn = {1529-2401}, doi = {10.1523/JNEUROSCI.3318-14.2015}, author = {Pachitariu, Marius and Lyamzin, Dmitry R and Sahani, Maneesh and Lesica, Nicholas A} } @conference {48833, title = {Extracting regions of interest from biological images with convolutional sparse block coding.}, booktitle = {Neural Information Processing Systems (NIPS 2013)}, year = {2013}, month = {2013 Dec 05}, address = {Lake Tahoe}, abstract = {

Biological tissue is often composed of cells with similar morphologies replicated throughout large volumes and many biological applications rely on the accurate identification of these cells and their locations from image data. Here we develop a generative model that captures the regularities present in images composed of repeating elements of a few different types. Formally, the model can be described as convolutional sparse block coding. For inference we use a variant of convolutional matching pursuit adapted to block-based representations. We extend the K-SVD learning algorithm to subspaces by retaining several principal vectors from the SVD decomposition instead of just one. Good models with little cross-talk between subspaces can be obtained by learning the blocks incrementally. We perform extensive experiments on simulated images and the inference algorithm consistently recovers a large proportion of the cells with a small number of false positives. We fit the convolutional model to noisy GCaMP6 two-photon images of spiking neurons and to Nissl-stained slices of cortical tissue and show that it recovers cell body locations without supervision. The flexibility of the block-based representation is reflected in the variability of the recovered cell shapes.

}, url = {http://papers.nips.cc/paper/5167-extracting-regions-of-interest-from-biological-images-with-convolutional-sparse-block-coding}, author = {Pachitariu, Marius and Packer, Adam M and Pettit, Noah and Dalgleish, Henry and H{\"a}usser, Michael and Sahani, Maneesh} } @conference {48834, title = {Recurrent linear models of simultaneously-recorded neural populations.}, booktitle = {Neural Information Processing Systems (NIPS 2013)}, year = {2013}, month = {2013 Dec 05}, address = {Lake Tahoe}, abstract = {

Population neural recordings with long-range temporal structure are often best understood in terms of a shared underlying low-dimensional dynamical process. Advances in recording technology provide access to an ever larger fraction of the population, but the standard computational approaches available to identify the collective dynamics scale poorly with the size of the dataset. Here we describe a new, scalable approach to discovering the low-dimensional dynamics that underlie simultaneously recorded spike trains from a neural population. Our method is based on recurrent linear models (RLMs), and relates closely to timeseries models based on recurrent neural networks. We formulate RLMs for neural data by generalising the Kalman-filter-based likelihood calculation for latent linear dynamical systems (LDS) models to incorporate a generalised-linear observation process. We show that RLMs describe motor-cortical population data better than either directly-coupled generalised-linear models or latent linear dynamical system models with generalised-linear observations. We also introduce the cascaded linear model (CLM) to capture low-dimensional instantaneous correlations in neural populations. The CLM describes the cortical recordings better than either Ising or Gaussian models and, like the RLM, can be fit exactly and quickly. The CLM can also be seen as a generalization of a low-rank Gaussian model, in this case factor analysis. The computational tractability of the RLM and CLM allow both to scale to very high-dimensional neural data.

}, url = {http://papers.nips.cc/paper/4877-recurrent-linear-models-of-simultaneously-recorded-neural-populations}, author = {Pachitariu, Marius and Petreska, Biljana and Sahani, Maneesh} } @article {48841, title = {Regularization and nonlinearities for neural language models: when are they needed?}, journal = {arXiv}, year = {2013}, month = {2013 Jan 23}, pages = {arXiv:1301.5650}, abstract = {

Neural language models (LMs) based on recurrent neural networks (RNN) are some of the most successful word and character-level LMs. Why do they work so well, in particular better than linear neural LMs? Possible explanations are that RNNs have an implicitly better regularization or that RNNs have a higher capacity for storing patterns due to their nonlinearities or both. Here we argue for the first explanation in the limit of little training data and the second explanation for large amounts of text data. We show state-of-the-art performance on the popular and small Penn dataset when RNN LMs are regularized with random dropout. Nonetheless, we show even better performance from a simplified, much less expressive linear RNN model without off-diagonal entries in the recurrent matrix. We call this model an impulse-response LM (IRLM). Using random dropout, column normalization and annealed learning rates, IRLMs develop neurons that keep a memory of up to 50 words in the past and achieve a perplexity of 102.5 on the Penn dataset. On two large datasets however, the same regularization methods are unsuccessful for both models and the RNN{\textquoteright}s expressivity allows it to overtake the IRLM by 10 and 20 percent perplexity, respectively. Despite the perplexity gap, IRLMs still outperform RNNs on the Microsoft Research Sentence Completion (MRSC) task. We develop a slightly modified IRLM that separates long-context units (LCUs) from short-context units and show that the LCUs alone achieve a state-of-the-art performance on the MRSC task of 60.8\%. Our analysis indicates that a fruitful direction of research for neural LMs lies in developing more accessible internal representations, and suggests an optimization regime of very high momentum terms for effectively training such models.

}, url = {https://arxiv.org/abs/1301.5650}, author = {Pachitariu, Marius and Sahani, Maneesh} } @conference {48835, title = {Learning visual motion in recurrent neural networks.}, booktitle = {Neural Information Processing Systems (NIPS 2012)}, year = {2012}, month = {12/2003}, address = {Lake Tahoe}, abstract = {

We present a dynamic nonlinear generative model for visual motion based on a latent representation of binary-gated Gaussian variables. Trained on sequences of images, the model learns to represent different movement directions in different variables. We use an online approximate-inference scheme that can be mapped to the dynamics of networks of neurons. Probed with drifting grating stimuli and moving bars of light, neurons in the model show patterns of responses analogous to those of direction-selective simple cells in primary visual cortex. Most model neurons also show speed tuning and respond equally well to a range of motion directions and speeds aligned to the constraint line of their respective preferred speed. We show how these computations are enabled by a specific pattern of recurrent connections learned by the model.

}, url = {http://papers.nips.cc/paper/4814-learning-visual-motion-in-recurrent-neural-networks}, author = {Pachitariu, Marius and Sahani, Maneesh} }