Most downloaded biology preprints, all time
in category neuroscience
19,642 results found. For more information, click each entry to expand.
5,691 downloads bioRxiv neuroscience
Hierarchical temporal memory (HTM) provides a theoretical framework that models several key computational principles of the neocortex. In this paper we analyze an important component of HTM, the HTM spatial pooler (SP). The SP models how neurons learn feedforward connections and form efficient representations of the input. It converts arbitrary binary input patterns into sparse distributed representations (SDRs) using a combination of competitive Hebbian learning rules and homeostatic excitability control. We describe a number of key properties of the spatial pooler, including fast adaptation to changing input statistics, improved noise robustness through learning, efficient use of cells and robustness to cell death. In order to quantify these properties we develop a set of metrics that can be directly computed from the spatial pooler outputs. We show how the properties are met using these metrics and targeted artificial simulations. We then demonstrate the value of the spatial pooler in a complete end-to-end real-world HTM system. We discuss the relationship with neuroscience and previous studies of sparse coding. The HTM spatial pooler represents a neurally inspired algorithm for learning sparse representations from noisy data streams in an online fashion.
5,655 downloads bioRxiv neuroscience
The correct subcellular distribution of protein complexes establishes the complex morphology of neurons and is fundamental to their functioning. Thus, determining the dynamic distribution of proteins is essential to understand neuronal processes. Fluorescence imaging, in particular super-resolution microscopy, has become invaluable to investigate subcellular protein distribution. However, these approaches suffer from the limited ability to efficiently and reliably label endogenous proteins. We developed ORANGE: an Open Resource for the Application of Neuronal Genome Editing, that mediates targeted genomic integration of fluorescent tags in neurons. This toolbox includes a knock-in library for in-depth investigation of endogenous protein distribution, and a detailed protocol explaining how knock-in can be developed for novel targets. In combination with super-resolution microscopy, ORANGE revealed the dynamic nanoscale organization of endogenous neuronal signaling molecules, synaptic scaffolding proteins, and neurotransmitter receptors. Thus, ORANGE enables quantitation of expression and distribution for virtually any protein in neurons at high resolution and will significantly further our understanding of neuronal cell biology.
5,584 downloads bioRxiv neuroscience
Saskia E. J. de Vries, Jerome Lecoq, Michael A. Buice, Peter Groblewski, Gabriel K. Ocker, Michael Oliver, David Feng, Nicholas Cain, Peter Ledochowitsch, Daniel Millman, Kate Roll, Marina Garrett, Tom Keenan, Leonard Kuan, Stefan Mihalas, Shawn Olsen, Carol Thompson, Wayne Wakeman, Jack Waters, Derric Williams, Chris Barber, Nathan Berbesque, Brandon Blanchard, Nicholas Bowles, Shiella Caldejon, Linzy Casal, Andrew Cho, Sissy Cross, Chinh Dang, Tim Dolbeare, Melise Edwards, John Galbraith, Nathalie Gaudreault, Fiona Griffin, Perry Hargrave, Robert Howard, Lawrence Huang, Sean Jewell, Nika Keller, Ulf Knoblich, Josh Larkin, Rachael Larsen, Chris Lau, Eric Kenji Lee, Felix Lee, Arielle Leon, Lu Li, Fuhui Long, Jennifer Luviano, Kyla Mace, Thuyanh Nguyen, Jed Perkins, Miranda Robertson, Sam Seid, Eric Shea-Brown, Jianghong Shi, Nathan Sjoquist, Cliff Slaughterbeck, David Sullivan, Ryan Valenza, Casey White, Ali Williford, Daniela Witten, Jun Zhuang, Hongkui Zeng, Colin Farrell, Lydia Ng, Amy Bernard, John W. Phillips, R. Clay Reid, Christof Koch
To understand how the brain processes sensory information to guide behavior, we must know how stimulus representations are transformed throughout the visual cortex. Here we report an open, large-scale physiological survey of neural activity in the awake mouse visual cortex: the Allen Brain Observatory Visual Coding dataset. This publicly available dataset includes cortical activity from nearly 60,000 neurons collected from 6 visual areas, 4 layers, and 12 transgenic mouse lines from 221 adult mice, in response to a systematic set of visual stimuli. Using this dataset, we reveal functional differences across these dimensions and show that visual cortical responses are sparse but correlated. Surprisingly, responses to different stimuli are largely independent, e.g. whether a neuron responds to natural scenes provides no information about whether it responds to natural movies or to gratings. We show that these phenomena cannot be explained by standard local filter-based models, but are consistent with multi-layer hierarchical computation, as found in deeper layers of standard convolutional neural networks.
5,482 downloads bioRxiv neuroscience
This work presents a computational method for improving seizure detection for epilepsy diagnosis. Epilepsy is the second most common neurological disease impacting between 40 and 50 million of patients in the world and its proper diagnosis using electroencephalographic signals implies a long and expensive process which involves medical specialists. The proposed system is a patient-dependent offline system which performs an automatic detection of seizures in brainwaves applying a random forest classifier. Features are extracted using one-dimension reduced information from a spectro-temporal transformation of the biosignals which pass through an envelope detector. The performance of this method reached 97.12% of specificity, 99.29% of sensitivity, and a 0.77,h^-1 false positive rate. Thus, the method hereby proposed has great potential for diagnosis support in clinical environments.
5,472 downloads bioRxiv neuroscience
Neurons transmit information to distant brain regions via long-range axonal projections. In the mouse, area-to-area connections have only been systematically mapped using bulk labeling techniques, which obscure the diverse projections of intermingled single neurons. Here we describe MAPseq (Multiplexed Analysis of Projections by Sequencing), a technique that can map the projections of thousands or even millions of single neurons by labeling large sets of neurons with random RNA sequences ("barcodes"). Axons are filled with barcode mRNA, each putative projection area is dissected, and the barcode mRNA is extracted and sequenced. Applying MAPseq to the locus coeruleus (LC), we find that individual LC neurons have preferred cortical targets. By recasting neuroanatomy, which is traditionally viewed as a problem of microscopy, as a problem of sequencing, MAPseq harnesses advances in sequencing technology to permit high-throughput interrogation of brain circuits.
5,460 downloads bioRxiv neuroscience
Identifying low-dimensional features that describe large-scale neural recordings is a major challenge in neuroscience. Repeated temporal patterns (sequences) are thought to be a salient feature of neural dynamics, but are not succinctly captured by traditional dimensionality reduction techniques. Here we describe a software toolbox -- called seqNMF -- with new methods for extracting informative, non-redundant, sequences from high-dimensional neural data, testing the significance of these extracted patterns, and assessing the prevalence of sequential structure in data. We test these methods on simulated data under multiple noise conditions, and on several real neural and behavioral data sets. In hippocampal data, seqNMF identifies neural sequences that match those calculated manually by reference to behavioral events. In songbird data, seqNMF discovers neural sequences in untutored birds that lack stereotyped songs. Thus, by identifying temporal structure directly from neural data, seqNMF enables dissection of complex neural circuits without relying on temporal references from stimuli or behavioral outputs.
5,438 downloads bioRxiv neuroscience
Countries vary in their geographical and cultural properties. Only a few studies have explored how such variations influence how humans navigate or reason about space. We predicted that these variations impact human cognition, resulting in an organized spatial distribution of cognition at a planetary-wide scale. To test this hypothesis we developed a mobile-app-based cognitive task, measuring non-verbal spatial navigation ability in more than 2.5 million people, sampling populations in every nation state. We focused on spatial navigation due to its universal requirement across cultures. Using a clustering approach, we find that navigation ability is clustered into five distinct, yet geographically related, groups of countries. Specifically, the economic wealth of a nation was predictive of the average navigation ability of its inhabitants, and gender inequality was predictive of the size of performance difference between males and females. Thus, cognitive abilities, at least for spatial navigation, are clustered according to economic wealth and gender inequalities globally, which has significant implications for cross-cultural studies and multi-centre clinical trials using cognitive testing.
5,396 downloads bioRxiv neuroscience
Hippocampal neurons fire selectively in local behavioral contexts such as the position in an environment or phase of a task, and are thought to form a cognitive map of task-relevant variables. However, their activity varies over repeated behavioral conditions, such as different runs through the same position or repeated trials. Although widely observed across the brain, such variability is not well understood, and could reflect noise or structure, such as the encoding of additional cognitive information. Here, we introduce a conceptual model to explain variability in terms of underlying, population-level structure in single-trial neural activity. To test this model, we developed a novel unsupervised learning algorithm incorporating temporal dynamics, in order to characterize population activity as a trajectory on a nonlinear manifold--a space of possible network states. The manifold's structure captures correlations between neurons and temporal relationships between states, constraints arising from underlying network architecture and inputs. Using measurements of activity over time but no information about exogenous behavioral variables, we recovered hippocampal activity manifolds during spatial and non-spatial cognitive tasks in rats. Manifolds were low dimensional and smoothly encoded task-related variables, but contained an extra dimension reflecting information beyond the measured behavioral variables. Consistent with our model, neurons fired as a function of overall network state, and fluctuations in their activity across trials corresponded to variation in the underlying trajectory on the manifold. In particular, the extra dimension allowed the system to take different trajectories despite repeated behavioral conditions. Furthermore, the trajectory could temporarily decouple from current behavioral conditions and traverse neighboring manifold points corresponding to past, future, or nearby behavioral states. Our results suggest that trial-to-trial variability in the hippocampus is structured, and may reflect the operation of internal cognitive processes. The manifold structure of population activity is well-suited for organizing information to support memory, planning, and reinforcement learning. In general, our approach could find broader use in probing the organization and computational role of circuit dynamics in other brain regions.
5,353 downloads bioRxiv neuroscience
There is a vast array of new and improved methods for comparing groups and studying associations that offer the potential for substantially increasing power, providing improved control over the probability of a Type I error, and yielding a deeper and more nuanced under- standing of neuroscience data. These new techniques effectively deal with four insights into when and why conventional methods can be unsatisfactory. But for the non-statistician, the vast array of new and improved techniques for comparing groups and studying associations can seem daunting, simply because there are so many new methods that are now available. The paper briefly reviews when and why conventional methods can have relatively low power and yield misleading results. The main goal is to suggest some general guidelines regarding when, how and why certain modern techniques might be used.
5,328 downloads bioRxiv neuroscience
Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease primarily affecting motor neurons (MNs) to cause progressive paralysis. Ninety percent of cases are sporadic (sALS) and ten percent are familial (fALS). The molecular mechanisms underlying neurodegeneration remain elusive and there is a lack of promising biomarkers that define ALS phenotypes and progression. To date, most expression studies have focused on either complex whole tissues that contain cells other than MNs or induced pluripotent derived MNs (iMNs). Furthermore, as human tissue samples have high variability, estimation of differential gene-expression is not a trivial task. Here, we report a battery of orthogonal computational analyses to discover gene-expression defects in laser capture microdissected and enriched MN RNA pools from sALS patient spinal cords in regions destined for but not yet advanced in neurodegenerative stage. We used total RNA-sequencing (RNA-seq), applied multiple percentile rank (MPR) analysis to analyze MN-specific gene-expression signatures, and used high-throughput qPCR to validate RNA-seq results. Furthermore, we used a systems-level approach that identified molecular networks perturbed in sALS MNs. Weighted gene co-expression correlation network (WGCNA) analysis revealed defects in neurotransmitter biosynthesis and RNA-processing pathways while gene-gene interaction analysis showed abnormalities in networks that pertained to cell-adhesion, immune response and wound healing. We discover gene-expression signatures that distinguish sALS from control MNs and our findings illuminate possible mechanisms of cellular toxicity. Our systematic and comprehensive analysis serves as a framework to reveal expression signatures and disrupted pathways that will be useful for future mechanistic studies and biomarker based therapeutic research. *Corresponding authors: email@example.com, firstname.lastname@example.org
5,265 downloads bioRxiv neuroscience
Particular deep artificial neural networks (ANNs) are today's most accurate models of the primate brain's ventral visual stream. Here we report that, using a targeted ANN-driven image synthesis method, new luminous power patterns (i.e. images) can be applied to the primate retinae to predictably push the spiking activity of targeted V4 neural sites beyond naturally occurring levels. More importantly, this method, while not yet perfect, already achieves unprecedented independent control of the activity state of entire populations of V4 neural sites, even those with overlapping receptive fields. These results show how the knowledge embedded in today's ANN models might be used to non-invasively set desired internal brain states at neuron-level resolution, and suggest that more accurate ANN models would produce even more accurate control.
5,243 downloads bioRxiv neuroscience
Machine learning is a powerful set of techniques that has enhanced the abilities of neuroscientists to interpret information collected through EEG, fMRI, and MEG data. With these powerful techniques comes the danger of overfitting of hyper-parameters which can render results invalid, and cause a failure to generalize beyond the data set. We refer to this problem as ‘over-hyping’ and show that it is pernicious despite commonly used precautions. In particular, over-hyping occurs when an analysis is run repeatedly with slightly different analysis parameters and one set of results is selected based on the analysis. When this is done, the resulting method is unlikely to generalize to a new dataset, rendering it a partially, or perhaps even completely spurious result that will not be valid outside of the data used in the original analysis. While it is commonly assumed that cross-validation is an effective protection against such spurious results generated through overfitting or overhyping, this is not actually true. In this article, we show that both one-shot and iterative optimization of an analysis are prone to over-hyping, despite the use of cross-validation. We demonstrate that non-generalizable results can be obtained even on non-informative (i.e. random) data by modifying hyper-parameters in seemingly innocuous ways. We recommend a number of techniques for limiting over-hyping, such as lock-boxes, blind analyses, pre-registrations, and nested cross-validation. These techniques, are common in other fields that use machine learning, including computer science and physics. Adopting similar safeguards is critical for ensuring the robustness of machine-learning techniques in the neurosciences.
5,218 downloads bioRxiv neuroscience
Donald J. Hagler, Sean N Hatton, Carolina Makowski, M. Daniela Cornejo, Damien A. Fair, Anthony Steven Dick, Matthew T Sutherland, B.J. Casey, Deanna M. Barch, Michael P Harms, Richard Watts, James M. Bjork, Hugh P. Garavan, Laura Hilmer, Christopher J. Pung, Chelsea S. Sicat, Joshua Kuperman, Hauke Bartsch, Feng Xue, Mary M. Heitzeg, Angela R Laird, Thanh T. Trinh, Raul Gonzalez, Susan F. Tapert, Michael C Riedel, Lindsay M Squeglia, Luke W. Hyde, Monica D Rosenberg, Eric A. Earl, Katia D. Howlett, Fiona C. Baker, Mary Soules, Jazmin Diaz, Octavio Ruiz de Leon, Wesley K Thompson, Michael C. Neale, Megan Herting, Elizabeth R. Sowell, Ruben P. Alvarez, Samuel W. Hawes, Mariana Sanchez, Jerzy Bodurka, Florence J. Breslin, Amanda Sheffield Morris, Martin P. Paulus, W. Kyle Simmons, Jonathan R. Polimeni, Andre van der Kouwe, Andrew S. Nencka, Kevin M. Gray, Carlo Pierpaoli, John A. Matochik, Antonio Noronha, Will M. Aklin, Kevin Conway, Meyer Glantz, Elizabeth Hoffman, Roger Little, Marsha Lopez, Vani Pariyadath, Susan R.B. Weiss, Dana L. Wolff-Hughes, Rebecca DelCarmen-Wiggins, Sarah W. Feldstein Ewing, Oscar Miranda-Dominguez, Bonnie J. Nagel, Anders J. Perrone, Darrick T. Sturgeon, Aimee Goldstone, Adolf Pfefferbaum, Kilian M. Pohl, Devin Prouty, Kristina Uban, Susan Y Bookheimer, Mirella Dapretto, Adriana Galvan, Kara Bagot, Jay Giedd, M. Alejandra Infante, Joanna Jacobus, Kevin Patrick, Paul D Shilling, Rahul Desikan, Yi Li, Leo Sugrue, Marie T Banich, Naomi Friedman, John Hewitt, Christian Hopfer, Joseph Sakai, Jody Tanabe, Linda B. Cottler, Sara Jo Nixon, Linda Chang, Christine Cloak, Thomas Ernst, Gloria Reeves, David N. Kennedy, Steve Heeringa, Scott Peltier, John Schulenberg, Chandra Sripada, Robert A. Zucker, William G Iacono, Monica Luciana, Finnegan J. Calabro, Duncan B. Clark, David A. Lewis, Beatriz Luna, Claudiu Schirda, Tufikameni Brima, John J. Foxe, Edward G. Freedman, Daniel W. Mruzek, Michael J Mason, Rebekah Huber, Erin McGlade, Andrew Prescot, Perry F. Renshaw, Deborah A. Yurgelun-Todd, Nicholas A Allgaier, Julie A. Dumas, Masha Ivanova, Alexandra Potter, Paul Florsheim, Christine Larson, Krista Lisdahl, Michael E. Charness, Bernard Fuemmeler, John M. Hettema, Joel Steinberg, Andrey P Anokhin, Paul Glaser, Andrew C. Heath, Pamela A. Madden, Arielle Baskin-Sommers, R. Todd Constable, Steven J. Grant, Gayathri J. Dowling, Sandra A. Brown, Terry L Jernigan, Anders M Dale
The Adolescent Brain Cognitive Development (ABCD) Study is an ongoing, nationwide study of the effects of environmental influences on behavioral and brain development in adolescents. The ABCD Study is a collaborative effort, including a Coordinating Center, 21 data acquisition sites across the United States, and a Data Analysis and Informatics Center (DAIC). The main objective of the study is to recruit and assess over eleven thousand 9-10-year-olds and follow them over the course of 10 years to characterize normative brain and cognitive development, the many factors that influence brain development, and the effects of those factors on mental health and other outcomes. The study employs state-of-the-art multimodal brain imaging, cognitive and clinical assessments, bioassays, and careful assessment of substance use, environment, psychopathological symptoms, and social functioning. The data will provide a resource of unprecedented scale and depth for studying typical and atypical development. Here, we describe the baseline neuroimaging processing and subject-level analysis methods used by the ABCD DAIC in the centralized processing and extraction of neuroanatomical and functional imaging phenotypes. Neuroimaging processing and analyses include modality-specific corrections for distortions and motion, brain segmentation and cortical surface reconstruction derived from structural magnetic resonance imaging (sMRI), analysis of brain microstructure using diffusion MRI (dMRI), task-related analysis of functional MRI (fMRI), and functional connectivity analysis of resting-state fMRI.
5,208 downloads bioRxiv neuroscience
Julia Marschallinger, Tal Iram, Macy Zardeneta, Song E Lee, Benoit Lehallier, Michael S. Haney, John V. Pluvinage, Vidhu Mathur, Oliver Hahn, David W Morgens, Justin Kim, Julia Tevini, Thomas K. Felder, Heimo Wolinski, Carolyn R Bertozzi, Michael C. Bassik, Ludwig Aigner, Tony Wyss-Coray
Microglia become progressively activated and seemingly dysfunctional with age, and genetic studies have linked these cells to the pathogenesis of a growing number of neurodegenerative diseases. Here we report a striking buildup of lipid droplets in microglia with aging in mouse and human brains. These cells, which we call lipid droplet-accumulating microglia (LAM), are defective in phagocytosis, produce high levels of reactive oxygen species, and secrete pro-inflammatory cytokines. RNA sequencing analysis of LAM revealed a transcriptional profile driven by innate inflammation distinct from previously reported microglial states. An unbiased CRISPR-Cas9 screen identified genetic modifiers of lipid droplet formation; surprisingly, variants of several of these genes, including progranulin, are causes of autosomal dominant forms of human neurodegenerative diseases. We thus propose that LAM contribute to age-related and genetic forms of neurodegeneration.
5,140 downloads bioRxiv neuroscience
Zizhen Yao, Thuc Nghi Nguyen, Cindy T.J. van Velthoven, Jeff Goldy, Adriana E. Sedeño-Cortés, Fahimeh Baftizadeh, Darren Bertagnolli, Tamara Casper, Kirsten Crichton, Song-Lin Ding, Olivia Fong, Emma Garren, Alexandra Glandon, James Gray, Lucas T Graybuck, Daniel Hirschstein, Matthew Kroll, Kanan Lathia, Boaz P. Levi, Delissa McMillen, Stephanie Mok, Thanh Pham, Qingzhong Ren, Christine Rimorin, Nadiya Shapovalova, Josef Sulc, Susan M. Sunkin, Michael Tieu, Amy Torkelson, Herman Tung, Katelyn Ward, Nick Dee, Kimberly Smith, Bosiljka Tasic, Hongkui Zeng
The isocortex and hippocampal formation are two major structures in the mammalian brain that play critical roles in perception, cognition, emotion and learning. Both structures contain multiple regions, for many of which the cellular composition is still poorly understood. In this study, we used two complementary single-cell RNA-sequencing approaches, SMART-Seq and 10x, to profile ~1.2 million cells covering all regions in the adult mouse isocortex and hippocampal formation, and derived a cell type taxonomy comprising 379 transcriptomic types. The completeness of coverage enabled us to define gene expression variations across the entire spatial landscape without significant gaps. We found that cell types are organized in a hierarchical manner and exhibit varying degrees of discrete or continuous relatedness with each other. Such molecular relationships correlate strongly with the spatial distribution patterns of the cell types, which can be region-specific, or shared across multiple regions, or part of one or more gradients along with other cell types. Glutamatergic neuron types have much greater diversity than GABAergic neuron types, both molecularly and spatially, and they define regional identities as well as inter-region relationships. For example, we found that glutamatergic cell types between the isocortex and hippocampal formation are highly distinct from each other yet possess shared molecular signatures and corresponding layer specificities, indicating their homologous relationships. Overall, our study establishes a molecular architecture of the mammalian isocortex and hippocampal formation for the first time, and begins to shed light on its underlying relationship with the development, evolution, connectivity and function of these two brain structures.
5,123 downloads bioRxiv neuroscience
Hanchuan Peng, Xie Peng, Lijuan Liu, Xiuli Kuang, Yimin Wang, Lei Qu, Hui Gong, Shengdian Jiang, Anan Li, Zongcai Ruan, Liya Ding, Chao Chen, Mengya Chen, Tanya L. Daigle, Zhangcan Ding, Yanjun Duan, Aaron Feiner, Ping He, Chris Hill, Karla E. Hirokawa, Guodong Hong, Lei Huang, Sara Kebede, Hsien-Chi Kuo, Rachael Larsen, Phil Lesnar, Longfei Li, Qi Li, Xiangning Li, Yaoyao Li, Yuanyuan Li, An Liu, Donghuan Lu, Stephanie Mok, Lydia Ng, Thuc Nghi Nguyen, Qiang Ouyang, Jintao Pan, Elise Shen, Yuanyuan Song, Susan M. Sunkin, Bosiljka Tasic, Matthew B Veldman, Wayne Wakeman, Wan Wan, Peng Wang, Quanxin Wang, Tao Wang, Yaping Wang, Feng Xiong, Wei Xiong, Wenjie Xu, Zizhen Yao, Min Ye, Lulu Yin, Yang Yu, Jia Yuan, Jing Yuan, Zhixi Yun, Shaoqun Zeng, Shichen Zhang, Sujun Zhao, Zijun Zhao, Zhi Zhou, Z. Josh Huang, Luke Esposito, Michael J. Hawrylycz, Staci Sorensen, X. William Yang, Yefeng Zheng, Zhongze Gu, Wei Xie, Christof Koch, Qingming Luo, Julie A. Harris, Yun Wang, Hongkui Zeng
Ever since the seminal findings of Ramon y Cajal, dendritic and axonal morphology has been recognized as a defining feature of neuronal types. Yet our knowledge concerning the diversity of neuronal morphologies, in particular distal axonal projection patterns, is extremely limited. To systematically obtain single neuron full morphology on a brain-wide scale, we established a platform with five major components: sparse labeling, whole-brain imaging, reconstruction, registration, and classification. We achieved sparse, robust and consistent fluorescent labeling of a wide range of neuronal types by combining transgenic or viral Cre delivery with novel transgenic reporter lines. We acquired high-resolution whole-brain fluorescent images from a large set of sparsely labeled brains using fluorescence micro-optical sectioning tomography (fMOST). We developed a set of software tools for efficient large-volume image data processing, registration to the Allen Mouse Brain Common Coordinate Framework (CCF), and computer-assisted morphological reconstruction. We reconstructed and analyzed the complete morphologies of 1,708 neurons from the striatum, thalamus, cortex and claustrum. Finally, we classified these cells into multiple morphological and projection types and identified a set of region-specific organizational rules of long-range axonal projections at the single cell level. Specifically, different neuron types from different regions follow highly distinct rules in convergent or divergent projection, feedforward or feedback axon termination patterns, and between-cell homogeneity or heterogeneity. Major molecularly defined classes or types of neurons have correspondingly distinct morphological and projection patterns, however, we also identify further remarkably extensive morphological and projection diversity at more fine-grained levels within the major types that cannot presently be accounted for by preexisting transcriptomic subtypes. These insights reinforce the importance of full morphological characterization of brain cell types and suggest a plethora of ways different cell types and individual neurons may contribute to the function of their respective circuits. ### Competing Interest Statement The authors have declared no competing interest.
5,084 downloads bioRxiv neuroscience
Electrical recordings from a large array of electrodes give us access to neural population activity with single-cell, single-spike resolution. These recordings contain extracellular spikes which must be correctly detected and assigned to individual neurons. Despite numerous spike-sorting techniques developed in the past, a lack of high-quality ground-truth datasets hinders the validation of spike-sorting approaches. Furthermore, existing approaches requiring manual corrections are not scalable for hours of recordings exceeding 100 channels. To address these issues, we built a comprehensive spike-sorting pipeline that performs reliably under noise and probe drift by incorporating covariance-based features and unsupervised clustering based on fast density-peak finding. We validated performance of our workflow using multiple ground-truth datasets that recently became available. Our software scales linearly and processes up to 1000-channel recording in real-time using a single workstation. Accurate, real-time spike sorting from large recording arrays will enable more precise control of closed-loop feedback experiments and brain-computer interfaces.
5,041 downloads bioRxiv neuroscience
Developments in microfabrication technology have enabled the production of neural electrode arrays with hundreds of closely-spaced recording sites, and electrodes with thousands of sites are currently under development. These probes will in principle allow the simultaneous recording of very large numbers of neurons. However, use of this technology requires the development of techniques for decoding the spike times of the recorded neurons, from the raw data captured from the probes. There currently exists no practical solution to this problem of “spike sorting” for large, dense electrode arrays. Here, we present a set of novel tools to solve this problem, implemented in a suite of practical, user-friendly, open-source software. We validate these methods on data from rat cortex, demonstrating error rates as low as 5%.
5,013 downloads bioRxiv neuroscience
Jonathan S. Marvin, Yoshiteru Shimoda, Vincent Malgoire, Marco Leite, Takashi Kawashima, Thomas P. Jensen, Erika L Knott, Ondrej Novak, Kaspar Podgorski, Nancy J Leidenheimer, Dmitri A Rusakov, Misha B. Ahrens, Dimitri M Kullmann, Loren L. Looger
Current techniques for monitoring GABA, the primary inhibitory neurotransmitter in vertebrates, cannot follow ephemeral transients in intact neural circuits. We applied the design principles used to create iGluSnFR, a fluorescent reporter of synaptic glutamate, to develop a GABA sensor using a protein derived from a previously unsequenced Pseudomonas fluorescens strain. Structure-guided mutagenesis and library screening led to a usable iGABASnFR (maximum DeltaF/F ~ 2.5, Kd ~ 9 micromolar, good specificity, adequate kinetics). iGABASnFR is genetically encoded, detects single action potential-evoked GABA release events in culture, and produces readily detectable fluorescence increases in vivo in mice and zebrafish. iGABASnFR enabled tracking of: (1) mitochondrial GABA content and its modulation by an anticonvulsant; (2) swimming-evoked GABAergic transmission in zebrafish cerebellum; (3) GABA release events during inter-ictal spikes and seizures in awake mice; and (4) GABAergic tone decreases during isoflurane anesthesia. iGABASnFR will permit high spatiotemporal resolution of GABA signaling in intact preparations.
4,993 downloads bioRxiv neuroscience
Genetically encoded fluorescent proteins and immunostainings are widely used to detect cellular or sub-cellular structures in thick biological samples. However, each approach suffers from limitations, including low signal and limited spectral flexibility or slow speed, poor penetration and high background, respectively. Here we overcome these limitations by using transgenically expressed chemical tags for rapid, even and low-background labeling of thick biological tissues. We construct a platform of widely applicable transgenic Drosophila reporter lines, demonstrating that chemical labeling can accelerate staining of whole-mount fly brains by a factor of 100x. Together, this tag-based approach drastically improves the speed and specificity of labeling genetically marked cells in intact and/or thick biological samples.
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