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Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 62,482 bioRxiv papers from 277,419 authors.

Most downloaded bioRxiv papers, all time

in category neuroscience

10,871 results found. For more information, click each entry to expand.

61: Ultra fast tissue staining with chemical tags
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Posted to bioRxiv 19 May 2014

Ultra fast tissue staining with chemical tags
4,500 downloads neuroscience

Johannes Kohl, Julian Ng, Sebastian Cachero, Michael-John Dolan, Ben Sutcliffe, Daniel Krueger, Shahar Frechter, Gregory S.X.E. Jefferis

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.

62: Tractography-based connectomes are dominated by false-positive connections
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Posted to bioRxiv 07 Nov 2016

Tractography-based connectomes are dominated by false-positive connections
4,447 downloads neuroscience

Klaus 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, Fabrizio 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.

63: Filter-banks and artificial intelligence in seizure detection using electroencephalograms
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Posted to bioRxiv 03 Feb 2017

Filter-banks and artificial intelligence in seizure detection using electroencephalograms
4,404 downloads neuroscience

M. A. Pinto-Orellana, F. R. Cerqueira

Epilepsy is the most typical neurological disease in the world, and it implies an expensive and specialized diagnosis process based on electroencephalograms and video recordings. We developed a method that only requires the brainwave provided by the difference between two standard-located electrodes. Our proposed technique separates the original signal using a filter array with three different types of filters, and then extracts several features based on information theory and statistical information. In our study, we found that only 10 characteristics, of which the most important are related to higher frequencies, are required to offer an accuracy of 94%, a specificity of 95% and a sensitivity of 87% using C4.5 decision trees.

64: Gene Expression Signatures of Sporadic ALS Motor Neuron Populations
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Posted to bioRxiv 01 Feb 2016

Gene Expression Signatures of Sporadic ALS Motor Neuron Populations
4,398 downloads neuroscience

Ranjan Batra, Kasey Hutt, Anthony Vu, Stuart J Rabin, Michael W Baughn, Ryan T Libby, Shawn Hoon, John Ravits, Gene W Yeo

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: geneyeo@ucsd.edu, jravits@ucsd.edu

65: Advances And Perspectives In Tissue Clearing Using CLARITY
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Posted to bioRxiv 02 Jun 2017

Advances And Perspectives In Tissue Clearing Using CLARITY
4,396 downloads neuroscience

Kristian H. Reveles Jensen, Rune W. Berg

CLARITY is a tissue clearing method, which enables immunostaining and imaging of large volumes for 3D-reconstruction. The method was initially time-consuming, expensive and relied on electrophoresis to remove lipids to make the tissue transparent. Since then several improvements and simplifications have emerged, such as passive clearing (PACT) and methods to improve tissue staining. Here, we review advances and compare current applications with the aim of highlighting needed improvements as well as aiding selection of the specific protocol for use in future investigations.

66: Spatial organization of the somatosensory cortex revealed by cyclic smFISH
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Posted to bioRxiv 04 Mar 2018

Spatial organization of the somatosensory cortex revealed by cyclic smFISH
4,359 downloads neuroscience

Simone Codeluppi, Lars E Borm, Amit Zeisel, Gioele La Manno, Josina A van Lunteren, Camilla I Svensson, Sten Linnarsson

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.

67: Conserved cell types with divergent features between human and mouse cortex
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Posted to bioRxiv 05 Aug 2018

Conserved cell types with divergent features between human and mouse cortex
4,359 downloads neuroscience

Rebecca D Hodge, Trygve E Bakken, Jeremy A Miller, Kimberly A Smith, Eliza R Barkan, Lucas T Graybuck, Jennie L Close, Brian Long, Osnat Penn, Zizhen Yao, Jeroen Eggermont, Thomas Hollt, Boaz P Levi, Soraya I Shehata, Brian Aevermann, Allison Beller, Darren Bertagnolli, Krissy Brouner, Tamara Casper, Charles Cobbs, Rachel Dalley, Nick Dee, Song-Lin Ding, Richard G Ellenbogen, Olivia Fong, Emma Garren, Jeff Goldy, Ryder P Gwinn, Daniel Hirschstein, C Dirk Keene, Mohamed Keshk, Andrew L Ko, Kanan Lathia, Ahmed Mahfouz, Zoe Maltzer, Medea McGraw, Thuc Nghi Nguyen, Julie Nyhus, Jeffrey G Ojemann, Aaron Oldre, Sheana Parry, Shannon Reynolds, Christine Rimorin, Nadiya V Shapovalova, Saroja Somasundaram, Aaron Szafer, Elliot R Thomsen, Michael Tieu, Richard H Scheuermann, Rafael Yuste, Susan M Sunkin, Boudewijn Lelieveldt, David Feng, Lydia Ng, Amy Bernard, Michael Hawrylycz, John W Phillips, Bosiljka Tasic, Hongkui Zeng, Allan R Jones, Christof Koch, Ed S Lein

Elucidating the cellular architecture of the human neocortex is central to understanding our cognitive abilities and susceptibility to disease. Here we applied single nucleus RNA-sequencing to perform a comprehensive analysis of cell types in the middle temporal gyrus of human cerebral cortex. We identify a highly diverse set of excitatory and inhibitory neuronal types that are mostly sparse, with excitatory types being less layer-restricted than expected. Comparison to a similar mouse cortex single cell RNA-sequencing dataset revealed a surprisingly well-conserved cellular architecture that enables matching of homologous types and predictions of human cell type properties. Despite this general conservation, we also find extensive differences between homologous human and mouse cell types, including dramatic alterations in proportions, laminar distributions, gene expression, and morphology. These species-specific features emphasize the importance of directly studying human brain.

68: Aged blood inhibits hippocampal neurogenesis and activates microglia through VCAM1 at the blood-brain barrier
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Posted to bioRxiv 03 Jan 2018

Aged blood inhibits hippocampal neurogenesis and activates microglia through VCAM1 at the blood-brain barrier
4,331 downloads 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.

69: Optical clearing of living brains with MAGICAL to extend in vivo imaging
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Posted to bioRxiv 09 Jan 2019

Optical clearing of living brains with MAGICAL to extend in vivo imaging
4,276 downloads neuroscience

Kouichirou Iijima, Takuto Oshima, Ryosuke Kawakami, Tomomi Nemoto

To understand brain functions, it is important to observe directly how multiple neural circuits are performing in living brains. However, due to tissue opaqueness, observable depth and spatiotemporal resolution are severely degraded in vivo. Here, we propose an optical brain clearing method for in vivo fluorescence microscopy, termed MAGICAL (Magical Additive Glycerol Improves Clear Alive Luminance). MAGICAL enabled two-photon microscopy to capture vivid images with fast speed, at cortical layer V and hippocampal CA1 in vivo. Moreover, MAGICAL promoted conventional confocal microscopy to visualize finer neuronal structures including synaptic boutons and spines in unprecedented deep regions, without intensive illumination leading to phototoxic effects. Fluorescence Emission Spectrum Transmissive Analysis (FESTA) showed that MAGICAL improved in vivo transmittance of shorter wavelength light, which is vulnerable to optical scattering thus unsuited for in vivo microscopy. These results suggest that MAGICAL would transparentize living brains via scattering reduction.

70: A guide to robust statistical methods in neuroscience
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Posted to bioRxiv 20 Jun 2017

A guide to robust statistical methods in neuroscience
4,208 downloads neuroscience

Rand R. Wilcox, Guillaume A. Rousselet

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.

71: Neural Population Control via Deep Image Synthesis
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Posted to bioRxiv 04 Nov 2018

Neural Population Control via Deep Image Synthesis
4,144 downloads neuroscience

Pouya Bashivan, Kohitij Kar, James J DiCarlo

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.

72: A genetically encoded fluorescent sensor for in vivo imaging of GABA
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Posted to bioRxiv 14 May 2018

A genetically encoded fluorescent sensor for in vivo imaging of GABA
4,129 downloads 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.

73: A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex
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Posted to bioRxiv 29 Jun 2018

A large-scale, standardized physiological survey reveals higher order coding throughout the mouse visual cortex
4,124 downloads neuroscience

Saskia E. J. de Vries, Jerome Lecoq, Michael A. Buice, Peter A. 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 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.

74: All-optical electrophysiology reveals brain-state dependent changes in hippocampal subthreshold dynamics and excitability
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Posted to bioRxiv 13 Mar 2018

All-optical electrophysiology reveals brain-state dependent changes in hippocampal subthreshold dynamics and excitability
4,076 downloads neuroscience

Yoav Adam, Jeong J Kim, Shan Lou, Yongxin Zhao, Daan Brinks, Hao Wu, Mohammed A Mostajo-Radji, Simon Kheifets, Vicente Parot, Selmaan Chettih, Katherine J Williams, Samouil L Farhi, Linda Madisen, Christopher D Harvey, Hongkui Zeng, Paola Arlotta, Robert E Campbell, Adam E Cohen

A technology to record membrane potential from multiple neurons, simultaneously, in behaving animals will have a transformative impact on neuroscience research. Parallel recordings could reveal the subthreshold potentials and intercellular correlations that underlie network behavior. Paired stimulation and recording can further reveal the input-output properties of individual cells or networks in the context of different brain states. Genetically encoded voltage indicators are a promising tool for these purposes, but were so far limited to single-cell recordings with marginal signal to noise ratio (SNR) in vivo. We developed improved near infrared voltage indicators, high speed microscopes and targeted gene expression schemes which enabled recordings of supra- and subthreshold voltage dynamics from multiple neurons simultaneously in mouse hippocampus, in vivo. The reporters revealed sub-cellular details of back-propagating action potentials, correlations in sub-threshold voltage between multiple cells, and changes in dynamics associated with transitions from resting to locomotion. In combination with optogenetic stimulation, the reporters revealed brain state-dependent changes in neuronal excitability, reflecting the interplay of excitatory and inhibitory synaptic inputs. These tools open the possibility for detailed explorations of network dynamics in the context of behavior.

75: Single Cortical Neurons as Deep Artificial Neural Networks
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Posted to bioRxiv 18 Apr 2019

Single Cortical Neurons as Deep Artificial Neural Networks
4,059 downloads neuroscience

Beniaguev David, Segev Idan, London Michael

We propose a novel approach based on modern deep artificial neural networks (DNNs) for understanding how the morpho-electrical complexity of neurons shapes their input/output (I/O) properties at the millisecond resolution in response to massive synaptic input. The I/O of integrate and fire point neuron is accurately captured by a DNN with a single unit and one hidden layer. A fully connected DNN with one hidden layer faithfully replicated the I/O relationship of a detailed model of Layer 5 cortical pyramidal cell (L5PC) receiving AMPA and GABAA synapses. However, when adding voltage-gated NMDA-conductances, a temporally-convolutional DNN with seven layers was required. Analysis of the DNN filters provides new insights into dendritic processing shaping the I/O properties of neurons. This work proposes a systematic approach for characterizing the functional "depth" of a biological neurons, suggesting that cortical pyramidal neurons and the networks they form are computationally much more powerful than previously assumed.

76: A better way to define and describe Morlet wavelets for time-frequency analysis
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Posted to bioRxiv 21 Aug 2018

A better way to define and describe Morlet wavelets for time-frequency analysis
3,998 downloads neuroscience

Michael X Cohen

Morlet wavelets are frequently used for time-frequency analysis of non-stationary time series data, such as neuroelectrical signals recorded from the brain. The crucial parameter of Morlet wavelets is the width of the Gaussian that tapers the sine wave. This width parameter controls the trade-off between temporal precision and frequency precision. It is typically defined as the "number of cycles," but this parameter is opaque, and often leads to uncertainty and suboptimal analysis choices, as well as being difficult to interpret and evaluate. The purpose of this paper is to present alternative formulations of Morlet wavelets in time and in frequency that allow parameterizing the wavelets directly in terms of the desired temporal and spectral smoothing (as full-width at half-maximum). This formulation provides clarity on an important data analysis parameter, and should facilitate proper analyses, reporting, and interpretation of results. MATLAB code is provided.

77: Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models
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Posted to bioRxiv 03 Oct 2014

Fixed versus mixed RSA: Explaining visual representations by fixed and mixed feature sets from shallow and deep computational models
3,998 downloads neuroscience

Seyed-Mahdi Khaligh-Razavi, Linda Henriksson, Kendrick Kay, Nikolaus Kriegeskorte

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.

78: Developmental diversification of cortical inhibitory interneurons.
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Posted to bioRxiv 02 Feb 2017

Developmental diversification of cortical inhibitory interneurons.
3,888 downloads neuroscience

Christian Mayer, Christoph Hafemeister, Rachel C Bandler, Robert Machold, Kathryn Allaway, Xavier Jaglin, Renata Batista Brito, Andrew Butler, Gord Fishell, Rahul Satija

Diverse subsets of cortical interneurons play a particularly important role in the stability of the neural circuits underlying cognitive and higher order brain functions, yet our understanding of how this diversity is generated is far from complete. We applied massively parallel single-cell RNA-seq to profile a developmental time course of interneuron development, measuring the transcriptomes of over 60,000 progenitors during their maturation in the ganglionic eminences and embryonic migration into the cortex. While diversity within mitotic progenitors is largely driven by cell cycle and differentiation state, we observed sparse eminence-specific transcription factor expression, which seeds the emergence of later cell diversity. Upon becoming postmitotic, cells from all eminences pass through one of three precursor states, one of which represents a cortical interneuron ground state. By integrating datasets across developmental timepoints, we identified transcriptomic heterogeneity in interneuron precursors representing the emergence of four cardinal classes (Pvalb, Sst, Id2 and Vip), which further separate into subtypes at different timepoints during development. Our analysis revealed that the ASD-associated transcription factor Mef2c discriminates early Pvalb-precursors in E13.5 cells, and removal of Mef2c confirms its essential role for Pvalb interneuron development. These findings shed new light on the molecular diversification of early inhibitory precursors, and suggest gene modules that may link developmental specification with the etiology of neuropsychiatric disorders.

79: Light Sheet Theta Microscopy for High-resolution Quantitative Imaging of Large Biological Systems
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Posted to bioRxiv 22 Mar 2017

Light Sheet Theta Microscopy for High-resolution Quantitative Imaging of Large Biological Systems
3,871 downloads neuroscience

Bianca Migliori, Malika S Datta, Christophe Dupre, Mehmet C Apak, Shoh Asano, Ruixuan Gao, Edward S Boyden, Ola Hermanson, Rafael Yuste, Raju Tomer

Advances in tissue clearing and molecular labelling methods are enabling unprecedented optical access to large intact biological systems. These advances fuel the need for high-speed microscopy approaches to image large samples quantitatively and at high resolution. While Light Sheet Microscopy (LSM), with its high planar imaging speed and low photo-bleaching, can be effective, scaling up to larger imaging volumes has been hindered by the use of orthogonal light-sheet illumination. To address this fundamental limitation, we have developed Light Sheet Theta Microscopy (LSTM), which uniformly illuminates samples from same side as the detection objective, thereby eliminating limits on lateral dimensions without sacrificing imaging resolution, depth and speed. We present detailed characterization of LSTM, and show that this approach achieves rapid high-resolution imaging of large intact samples with superior uniform high-resolution than LSM. LSTM is a significant step in high-resolution quantitative mapping of structure and function of large intact biological systems.

80: Distributed correlates of visually-guided behavior across the mouse brain
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Posted to bioRxiv 20 Nov 2018

Distributed correlates of visually-guided behavior across the mouse brain
3,852 downloads neuroscience

Nicholas A Steinmetz, Peter Zatka-Haas, Matteo Carandini, Kenneth D Harris

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.

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