Most downloaded biology preprints, all time
in category neuroscience
19,641 results found. For more information, click each entry to expand.
4,983 downloads bioRxiv neuroscience
Omer Ali Bayraktar, Theresa Bartels, Damon Polioudakis, Staffan Holmqvist, Lucile Ben Haim, Adam M.H. Young, Kirti Prakash, Alexander Brown, Mercedes F. Paredes, Riki Kawaguchi, John Stockley, Khalida Sabeur, Sandra M. Chang, Eric Huang, Peter Hutchinson, Erik M. Ullian, Daniel H. Geschwind, Giovanni Coppola, David H. Rowitch
During organogenesis, patterns and gradients of gene expression underlie organization and diversified cell specification to generate complex tissue architecture. While the cerebral cortex is organized into six excitatory neuronal layers, it is unclear whether glial cells are diversified to mimic neuronal laminae or show distinct layering. To determine the molecular architecture of the mammalian cortex, we developed a high- content pipeline that can quantify single-cell gene expression in situ. The Large-area Spatial Transcriptomic (LaST) map confirmed expected cortical neuron layer organization and also revealed a novel neuronal identity signature. Screening 46 candidate genes for astrocyte diversity across the cortex, we identified grey matter superficial, mid and deep astrocyte identities in gradient layer patterns that were distinct from neurons. Astrocyte layers formed in early postnatal cortex and mostly persisted in adult mouse and human cortex. Mutations that shifted neuronal post-mitotic identity or organization were sufficient to alter glial layering, indicating an instructive role for neuronal cues. In normal mouse cortex, astrocyte layer patterns showed area diversity between functionally distinct cortical regions. These findings indicate that excitatory neurons and astrocytes cells are organized into distinct lineage-associated laminae, which give rise to higher order neuroglial complexity of cortical architecture.
4,962 downloads bioRxiv neuroscience
Modern recording techniques enable large-scale measurements of neural activity in a variety of model organisms. The dynamics of neural activity shed light on how organisms process sensory information and generate motor behavior. Here, we study these dynamics using optical recordings of neural activity in the nematode C. elegans. To understand these data, we develop state space models that decompose neural time-series into segments with simple, linear dynamics. We incorporate these models into a hierarchical framework that combines partial recordings from many worms to learn shared structure, while still allowing for individual variability. This framework reveals latent states of population neural activity, along with the discrete behavioral states that govern dynamics in this state space. We find stochastic transition patterns between discrete states and see that transition probabilities are determined by both current brain activity and sensory cues. Our methods automatically recover transition times that closely match manual labels of different behaviors, such as forward crawling, reversals, and turns. Finally, the resulting model can simulate neural data, faithfully capturing salient patterns of whole brain dynamics seen in real data.
4,957 downloads bioRxiv neuroscience
Stories play a fundamental role in human culture. They provide a mechanism for sharing cultural identity, imparting knowledge, revealing beliefs, reinforcing social bonds and providing entertainment that is central to all human societies. Here we investigated the extent to which the delivery medium (audio or visual) of a story affected conscious and sub-conscious engagement with the narrative. Although participants self-reported greater involvement for watching video relative to listening to auditory scenes, they had stronger physiological responses for auditory stories including higher heart rates, greater electrodermal activity, and even higher body temperatures. We interpret these findings as physiological evidence that the stories were more cognitively and emotionally engaging when presented in an auditory format. This may be because listening to a story is a more active process of co-creation (i.e. via imagination) than watching a video.
4,949 downloads bioRxiv neuroscience
Michael N Economo, Sarada Viswanathan, Bosiljka Tasic, Erhan Bas, Johan Winnubst, Vilas Menon, Lucas T. Graybuck, Thuc Nghi Nguyen, Lihua Wang, Charles R. Gerfen, Jayaram Chandrashekar, Hongkui Zeng, Loren L. Looger, Karel Svoboda
Activity in motor cortex predicts specific movements, seconds before they are initiated. This preparatory activity has been observed in L5 descending "pyramidal tract" (PT) neurons. A key question is how preparatory activity can be maintained without causing movement, and how preparatory activity is eventually converted to a motor command to trigger appropriate movements. We used single cell transcriptional profiling and axonal reconstructions to identify two types of PT neuron. Both types share projections to multiple targets in the basal ganglia and brainstem. One type projects to thalamic regions that connect back to motor cortex. In a delayed-response task, these neurons produced early preparatory activity that persisted until the movement. The second type projects to motor centers in the medulla and produced late preparatory activity and motor commands. These results indicate that two motor cortex output neurons are specialized for distinct roles in motor control.
4,943 downloads bioRxiv neuroscience
Hanadie Yousef, Cathrin J Czupalla, Davis Lee, Ashley Burke, Michelle Chen, Judith Zandstra, Elisabeth Berber, Benoit Lehallier, Vidhu Mathur, Ramesh V Nair, Liana Bonanno, Taylor Merkel, Markus Schwaninger, Stephen Quake, Eugene C Butcher, Tony Wyss-Coray
An aged circulatory environment can promote brain dysfunction and we hypothesized that the blood-brain barrier (BBB) mediates at least some of these effects. We observe brain endothelial cells (BECs) in the aged mouse hippocampus express an inflammatory transcriptional profile with focal upregulation of Vascular Cell Adhesion Molecule 1 (VCAM1), a protein that facilitates vascular-immune cell interactions. Concomitantly, the shed, soluble form of VCAM1 is prominently increased in the aged circulation of humans and mice, and aged plasma is sufficient to increase VCAM1 expression in cultured BECs and young mouse hippocampi. Systemic anti-VCAM1 antibody or genetic ablation of VCAM1 in BECs counteracts the detrimental effects of aged plasma on young brains and reverses aging aspects in old mouse brains. Thus, VCAM1 is a negative regulator of adult neurogenesis and inducer of microglial reactivity, establishing VCAM1 and the luminal side of the BBB as possible targets to treat age-related neurodegeneration.
4,935 downloads bioRxiv neuroscience
Studies of the primate visual system have begun to test a wide range of complex computational object-vision models. Realistic models have many parameters, which in practice cannot be fitted using the limited amounts of brain-activity data typically available. Task performance optimization (e.g. using backpropagation to train neural networks) provides major constraints for fitting parameters and discovering nonlinear representational features appropriate for the task (e.g. object classification). Model representations can be compared to brain representations in terms of the representational dissimilarities they predict for an image set. This method, called representational similarity analysis (RSA), enables us to test the representational feature space as is (fixed RSA) or to fit a linear transformation that mixes the nonlinear model features so as to best explain a cortical area's representational space (mixed RSA). Like voxel/population-receptive-field modelling, mixed RSA uses a training set (different stimuli) to fit one weight per model feature and response channel (voxels here), so as to best predict the response profile across images for each response channel. We analysed response patterns elicited by natural images, which were measured with functional magnetic resonance imaging (fMRI). We found that early visual areas were best accounted for by shallow models, such as a Gabor wavelet pyramid (GWP). The GWP model performed similarly with and without mixing, suggesting that the original features already approximated the representational space, obviating the need for mixing. However, a higher ventral-stream visual representation (lateral occipital region) was best explained by the higher layers of a deep convolutional network, and mixing of its feature set was essential for this model to explain the representation. We suspect that mixing was essential because the convolutional network had been trained to discriminate a set of 1000 categories, whose frequencies in the training set did not match their frequencies in natural experience or their behavioural importance. The latter factors might determine the representational prominence of semantic dimensions in higher-level ventral-stream areas. Our results demonstrate the benefits of testing both the specific representational hypothesis expressed by a model's original feature space and the hypothesis space generated by linear transformations of that feature space.
4,924 downloads bioRxiv neuroscience
Deep artificial neural networks with spatially repeated processing (a.k.a., deep convolutional ANNs) have been established as the best class of candidate models of visual processing in primate ventral visual processing stream. Over the past five years, these ANNs have evolved from a simple feedforward eight-layer architecture in AlexNet to extremely deep and branching NASNet architectures, demonstrating increasingly better object categorization performance and increasingly better explanatory power of both neural and behavioral responses. However, from the neuroscientist's point of view, the relationship between such very deep architectures and the ventral visual pathway is incomplete in at least two ways. On the one hand, current state-of-the-art ANNs appear to be too complex (e.g., now over 100 levels) compared with the relatively shallow cortical hierarchy (4-8 levels), which makes it difficult to map their elements to those in the ventral visual stream and to understand what they are doing. On the other hand, current state-of-the-art ANNs appear to be not complex enough in that they lack recurrent connections and the resulting neural response dynamics that are commonplace in the ventral visual stream. Here we describe our ongoing efforts to resolve both of these issues by developing a "CORnet" family of deep neural network architectures. Rather than just seeking high object recognition performance (as the state-of-the-art ANNs above), we instead try to reduce the model family to its most important elements and then gradually build new ANNs with recurrent and skip connections while monitoring both performance and the match between each new CORnet model and a large body of primate brain and behavioral data. We report here that our current best ANN model derived from this approach (CORnet-S) is among the top models on Brain-Score, a composite benchmark for comparing models to the brain, but is simpler than other deep ANNs in terms of the number of convolutions performed along the longest path of information processing in the model. All CORnet models are available at https://github.com/dicarlolab/CORnet, and we plan to update this manuscript and the available models in this family as they are produced.
4,896 downloads bioRxiv neuroscience
Behavior arises from neuronal activity, but it is not known how the active neurons are distributed across brain regions and how their activity unfolds in time. Here, we used high-density Neuropixels probes to record from ~30,000 neurons in mice performing a visual contrast discrimination task. The task activated 60% of the neurons, involving nearly all 42 recorded brain regions, well beyond the regions activated by passive visual stimulation. However, neurons selective for choice (left vs. right) were rare, and found mostly in midbrain, striatum, and frontal cortex. Those in midbrain were typically activated prior to contralateral choices and suppressed prior to ipsilateral choices, consistent with a competitive midbrain circuit for adjudicating the subject's choice. A brain-wide state shift distinguished trials in which visual stimuli led to movement. These results reveal concurrent representations of movement and choice in neurons widely distributed across the brain.
4,895 downloads bioRxiv neuroscience
The global efforts towards the creation of a molecular census of the brain using single-cell transcriptomics is generating a large catalog of molecularly defined cell types lacking spatial information. Thus, new methods are needed to map a large number of cell-specific markers simultaneously on large tissue areas. Here, we developed a cyclic single molecule fluorescence in situ hybridization methodology and defined the cellular organization of the somatosensory cortex using markers identified by single-cell transcriptomics.
4,881 downloads bioRxiv neuroscience
Klaus H. Maier-Hein, Peter Neher, Jean-Christophe Houde, Marc-Alexandre Côté, Eleftherios Garyfallidis, Jidan Zhong, Maxime Chamberland, Fang-Cheng Yeh, Ying-Chia Lin, Qing Ji, Wilburn E. Reddick, John O. Glass, David Qixiang Chen, Yuanjing Feng, Chengfeng Gao, Ye Wu, Jieyan Ma, H Renjie, Qiang Li, Carl-Fredrik Westin, Samuel Deslauriers-Gauthier, J. Omar Ocegueda González, Michael Paquette, Samuel St-Jean, Gabriel Girard, François Rheault, Jasmeen Sidhu, Chantal M.W. Tax, Fenghua Guo, Hamed Y. Mesri, Szabolcs Dávid, Martijn Froeling, Anneriet M. Heemskerk, Alexander Leemans, Arnaud Boré, Basile Pinsard, Christophe Bedetti, Matthieu Desrosiers, Simona Brambati, Julien Doyon, Alessia Sarica, Roberta Vasta, Antonio Cerasa, Aldo Quattrone, Jason Yeatman, Ali R. Khan, Wes Hodges, Simon Alexander, David Romascano, Muhamed Barakovic, Anna Auría, Oscar Esteban, Alia Lemkaddem, Jean-Philippe Thiran, H. Ertan Cetingul, Benjamin L. Odry, Boris Mailhe, Mariappan S. Nadar, F. Pizzagalli, Gautam Prasad, Julio E. Villalon-Reina, Justin Galvis, Paul M. Thompson, Francisco De Santiago Requejo, Pedro Luque Laguna, Luis Miguel Lacerda, Rachel Barrett, Flavio Dell’Acqua, Marco Catani, Laurent Petit, Emmanuel Caruyer, Alessandro Daducci, Tim B. Dyrby, Tim Holland-Letz, Claus C. Hilgetag, Bram Stieltjes, Maxime Descoteaux
Fiber tractography based on non-invasive diffusion imaging is at the heart of connectivity studies of the human brain. To date, the approach has not been systematically validated in ground truth studies. Based on a simulated human brain dataset with ground truth white matter tracts, we organized an open international tractography challenge, which resulted in 96 distinct submissions from 20 research groups. While most state-of-the-art algorithms reconstructed 90% of ground truth bundles to at least some extent, on average they produced four times more invalid than valid bundles. About half of the invalid bundles occurred systematically in the majority of submissions. Our results demonstrate fundamental ambiguities inherent to tract reconstruction methods based on diffusion orientation information, with critical consequences for the approach of diffusion tractography in particular and human connectivity studies in general.
4,842 downloads bioRxiv neuroscience
Identifying brain biomarkers of disease risk is a growing priority in neuroscience. The ability to identify meaningful biomarkers is limited by measurement reliability; unreliable measures are unsuitable for predicting clinical outcomes. Measuring brain activity using task-fMRI is a major focus of biomarker development; however, the reliability of task-fMRI has not been systematically evaluated. We present converging evidence demonstrating poor reliability of task-fMRI measures. First, a meta-analysis of 90 experiments (N=1,008) revealed poor overall reliability (mean ICC=.397). Second, the test-retest reliabilities of activity in a priori regions of interest across 11 common fMRI tasks collected in the context of the Human Connectome Project (N=45) and the Dunedin Study (N=20) were poor (ICCs=.067-.485). Collectively, these findings demonstrate that common task-fMRI measures are not currently suitable for brain biomarker discovery or individual differences research. We review how this state of affairs came to be and highlight avenues for improving task-fMRI reliability.
4,808 downloads bioRxiv neuroscience
Zizhen Yao, Hanqing Liu, Fangming Xie, Stephan Fischer, A. Sina Booeshaghi, Ricky S. Adkins, Andrew I. Aldridge, Seth A. Ament, Antonio Pinto-Duarte, Anna Bartlett, M. Margarita Behrens, Koen Van den Berge, Darren Bertagnolli, Tommaso Biancalani, Héctor Corrada Bravo, Tamara Casper, Carlo Colantuoni, Heather Creasy, Kirsten Crichton, Megan Crow, Nick Dee, Elizabeth L. Dougherty, Wayne I. Doyle, Sandrine Dudoit, Rongxin Fang, Victor Felix, Olivia Fong, Michelle Giglio, Jeff Goldy, Michael Hawrylycz, Hector Roux de Bezieux, Brian R. Herb, Ronna Hertzano, Xiaomeng Hou, Qiwen Hu, Jonathan Crabtree, Jayaram Kancherla, Matthew Kroll, Kanan Lathia, Yang Eric Li, Jacinta D. Lucero, Chongyuan Luo, Anup Mahurkar, Delissa McMillen, Naeem M. Nadaf, Joseph R. Nery, Sheng-Yong Niu, Joshua Orvis, Julia K. Osteen, Thanh Pham, Olivier Poirion, Sebastian Preissl, Elizabeth F Purdom, Christine Rimorin, Davide Risso, Angeline C. Rivkin, Kimberly Smith, Kelly Street, Josef Sulc, Thuc Nghi Nguyen, Michael Tieu, Amy Torkelson, Herman Tung, Eeshit Dhaval Vaishnav, Valentine Svensson, Charles R. Vanderburg, Vasilis Ntranos, Cindy T.J. van Velthoven, Xinxin Wang, Owen White, Z. Josh Huang, Peter V. Kharchenko, Lior Pachter, John Ngai, Aviv Regev, Bosiljka Tasic, Joshua D Welch, Jesse Gillis, Evan Macosko, Bing Ren, Joseph R. Ecker, Hongkui Zeng, Eran A Mukamel
Single cell transcriptomics has transformed the characterization of brain cell identity by providing quantitative molecular signatures for large, unbiased samples of brain cell populations. With the proliferation of taxonomies based on individual datasets, a major challenge is to integrate and validate results toward defining biologically meaningful cell types. We used a battery of single-cell transcriptome and epigenome measurements generated by the BRAIN Initiative Cell Census Network (BICCN) to comprehensively assess the molecular signatures of cell types in the mouse primary motor cortex (MOp). We further developed computational and statistical methods to integrate these multimodal data and quantitatively validate the reproducibility of the cell types. The reference atlas, based on more than 600,000 high quality single-cell or -nucleus samples assayed by six molecular modalities, is a comprehensive molecular account of the diverse neuronal and non-neuronal cell types in MOp. Collectively, our study indicates that the mouse primary motor cortex contains over 55 neuronal cell types that are highly replicable across analysis methods, sequencing technologies, and modalities. We find many concordant multimodal markers for each cell type, as well as thousands of genes and gene regulatory elements with discrepant transcriptomic and epigenomic signatures. These data highlight the complex molecular regulation of brain cell types and will directly enable design of reagents to target specific MOp cell types for functional analysis.
4,807 downloads bioRxiv neuroscience
Artificial neural networks have recently achieved many successes in solving sequential processing and planning tasks. Their success is often ascribed to the emergence of the task’s low-dimensional latent structure in the network activity – i.e., in the learned neural representations . Here, we investigate the hypothesis that a means for generating representations with easily accessed low-dimensional latent structure, possibly reflecting an underlying semantic organization, is through learning to predict observations about the world. Specifically, we ask whether and when network mechanisms for sensory prediction coincide with those for extracting the underlying latent variables. Using a recurrent neural network model trained to predict a sequence of observations we show that network dynamics exhibit low-dimensional but nonlinearly transformed representations of sensory inputs that map the latent structure of the sensory environment. We quantify these results using nonlinear measures of intrinsic dimensionality and linear decodability of latent variables, and provide mathematical arguments for why such useful p redictive representations emerge. We focus throughout on how our results can aid the analysis and interpretation of experimental data. ### Competing Interest Statement The authors have declared no competing interest.
4,779 downloads bioRxiv neuroscience
The neuroscience of perception has recently been revolutionized with an integrative reverse-engineering approach in which computation, brain function, and behavior are linked across many different datasets and many computational models. We here present a first systematic study taking this approach into higher-level cognition: human language processing, our species' signature cognitive skill. We find that the most powerful 'transformer' networks predict neural responses at nearly 100% and generalize across different datasets and data types (fMRI, ECoG). Across models, significant correlations are observed among all three metrics of performance: neural fit, fit to behavioral responses, and accuracy on the next-word prediction task (but not other language tasks), consistent with the long-standing hypothesis that the brain's language system is optimized for predictive processing. Model architectures with initial weights further perform surprisingly similar to final trained models, suggesting that inherent structure - and not just experience with language - crucially contributes to a model's match to the brain. ### Competing Interest Statement The authors have declared no competing interest.
4,771 downloads bioRxiv neuroscience
Kiryl D Piatkevich, Seth Bensussen, Hua-an Tseng, Sanaya N. Shroff, Violeta Gisselle Lopez-Huerta, Demian Park, Erica E. Jung, Or A. Shemesh, Christoph Straub, Howard J Gritton, Michael F. Romano, Emma Costa, Bernardo Sabatini, Zhanyan Fu, Edward S Boyden, Xue Han
A longstanding goal in neuroscience has been to image membrane voltage, with high temporal precision and sensitivity, in awake behaving mammals. Here, we report a genetically encoded voltage indicator, SomArchon, which exhibits millisecond response times and compatibility with optogenetic control, and which increases the sensitivity, signal-to-noise ratio, and number of neurons observable, by manyfold over previous reagents. SomArchon only requires conventional one-photon microscopy to achieve these high performance characteristics. These improvements enable population analysis of neural activity, both at the subthreshold and spiking levels, in multiple brain regions: cortex, hippocampus, and striatum of awake behaving mice. Using SomArchon, we detect both positive and negative responses of striatal neurons during movement, highlighting the power of voltage imaging to reveal bidirectional modulation. We also examine how the intracellular subthreshold theta oscillations of hippocampal neurons govern spike output, finding that nearby cells can exhibit highly correlated subthreshold activities, even as they generate highly divergent spiking patterns.
4,748 downloads bioRxiv neuroscience
Mariya Chavarha, Vincent Villette, Ivan K. Dimov, Lagnajeet Pradhan, Stephen W. Evans, Dongqing Shi, Renzhi Yang, Simon Chamberland, Jonathan Bradley, Benjamin Mathieu, Francois St-Pierre, Mark J Schnitzer, Guoqiang Bi, Katalin Toth, Jun Ding, Stéphane Dieudonné, Michael Lin
Imaging of transmembrane voltage deep in brain tissue with cellular resolution has the potential to reveal information processing by neuronal circuits in living animals with minimal perturbation. Multi-photon voltage imaging in vivo, however, is currently limited by speed and sensitivity of both indicators and imaging methods. Here, we report the engineering of an improved genetically encoded voltage indicator, ASAP3, which exhibits up to 51% fluorescence responses in the physiological voltage range, sub-millisecond activation kinetics, and full responsivity under two-photon illumination. We also introduce an ultrafast local volume excitation (ULOVE) two-photon scanning method to sample ASAP3 signals in awake mice at kilohertz rates with increased stability and sensitivity. ASAP3 and ULOVE allowed continuous single-trial tracking of spikes and subthreshold events for minutes in deep locations, with subcellular resolution, and with repeated sampling over multiple days. By imaging voltage in visual cortex neurons, we found evidence for cell type-dependent subthreshold modulation by locomotion. Thus, ASAP3 and ULOVE enable continuous high-speed high-resolution imaging of electrical activity in deeply located genetically defined neurons during awake behavior.
4,748 downloads bioRxiv neuroscience
Rapid phasic activity of midbrain dopamine neurons are thought to signal reward prediction errors (RPEs), resembling temporal difference errors used in machine learning. Recent studies describing slowly increasing dopamine signals have instead proposed that they represent state values and arise independently from somatic spiking activity. Here, we developed novel experimental paradigms using virtual reality that disambiguate RPEs from values. We examined the dopamine circuit activity at various stages including somatic spiking, axonal calcium signals, and striatal dopamine concentrations. Our results demonstrate that ramping dopamine signals are consistent with RPEs rather than value, and this ramping is observed at all the stages examined. We further show that ramping dopamine signals can be driven by a dynamic stimulus that indicates a gradual approach to a reward. We provide a unified computational understanding of rapid phasic and slowly ramping dopamine signals: dopamine neurons perform a derivative-like computation over values on a moment-by-moment basis.
4,747 downloads bioRxiv neuroscience
Andrea Giovannucci, Johannes Friedrich, Pat Gunn, Jérémie Kalfon, Sue Ann Koay, Jiannis Taxidis, Farzaneh Najafi, Jeffrey L. Gauthier, Pengcheng Zhou, David W Tank, Dmitri Chklovskii, Eftychios A. Pnevmatikakis
Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. Here we present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good performance on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn, we collected a corpus of ground truth annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.
4,716 downloads bioRxiv neuroscience
Wearable eye-trackers offer exciting advantages over screen-based systems, but their use in research settings has been hindered by significant analytic challenges as well as a lack of published performance measures among competing devices on the market. In this article, we address both of these limitations. We describe (and make freely available) an automated analysis pipeline for mapping gaze data from an egocentric coordinate system (i.e. the wearable eye-tracker) to a fixed reference coordinate system (i.e. a target stimulus in the environment). This pipeline allows researchers to study aggregate viewing behavior on a 2D planar target stimulus without restricting the mobility of participants. We also designed a task to directly compare calibration accuracy and precision across 3 popular models of wearable eye-trackers: Pupil Labs 120Hz Binocular glasses, SMI ETG 2 glasses, and the Tobii Pro Glasses 2. Our task encompassed multiple viewing conditions selected to approximate distances and gaze angles typical for short- to mid-range viewing experiments. This work will promote and facilitate the use of wearable eye-trackers for research in naturalistic viewing experiments.
4,707 downloads bioRxiv neuroscience
Nathan William Gouwens, Staci Sorensen, Jim Berg, Changkyu Lee, Tim Jarsky, Jonathan T Ting, Susan M. Sunkin, David Feng, Costas Anastassiou, Eliza Barkan, Kris Bickley, Nicole Blesie, Thomas Braun, Krissy Brouner, Agata Budzillo, Shiella Caldejon, Tamara Casper, Dan Casteli, Peter Chong, Kirsten Crichton, Christine Cuhaciyan, Tanya L. Daigle, Rachel Dalley, Nick Dee, Tsega Desta, Samuel Dingman, Alyse Doperalski, Nadezhda Dotson, Tom Egdorf, Michael Fisher, Rebecca A de Frates, Emma Garren, Marissa Garwood, Amanda Gary, Nathalie Gaudreault, Keith Godfrey, Melissa Gorham, Thuc Nghi Nguyen, Caroline Habel, Kristen Hadley, James Harrington, Julie Harris, Alex Henry, DiJon Hill, Sam Josephsen, Sara Kebede, Lisa Kim, Matthew Kroll, Brian R Lee, Tracy Lemon, Xiaoxiao Liu, Brian Long, Rusty Mann, Medea McGraw, Stefan Mihalas, Alice Mukora, Gabe Murphy, Lindsay Ng, Kiet Ngo, Philip R. Nicovich, Aaron Oldre, Daniel Park, Sheana Parry, Jed Perkins, Lydia Potekhina, David Reid, Miranda Robertson, David Sandman, Martin Schroedter, Cliff Slaughterbeck, Gilberto Soler-Llavina, Josef Sulc, Aaron Szafer, Bosiljka Tasic, Naz Taskin, Corinne Teeter, Nivretta Thatra, Herman Tung, Wayne Wakeman, Grace Williams, Rob Young, Zhi Zhou, Colin Farrell, Hanchuan Peng, Michael Hawrylycz, Ed S Lein, Lydia Ng, Anton Arkhipov, Amy Bernard, John W. Phillips, Hongkui Zeng, Christof Koch
Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To profile morpho-electric properties of mammalian neurons systematically, we established a single cell characterization pipeline using standardized patch clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly-accessible online database, the Allen Cell Types Database, to display these data sets. Intrinsic physiological and morphological properties were measured from over 1,800 neurons from the adult laboratory mouse visual cortex. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We establish a taxonomy of morphologically- and electrophysiologically-defined cell types for this region of cortex with 17 e-types and 35 m-types, as well as an initial correspondence with previously-defined transcriptomic cell types using the same transgenic mouse lines.
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