Rxivist combines preprints from bioRxiv with data from Twitter to help you find the papers being discussed in your field. Currently indexing 94,912 bioRxiv papers from 404,161 authors.
Most downloaded bioRxiv papers, all time
in category neuroscience
16,549 results found. For more information, click each entry to expand.
5,754 downloads neuroscience
Johan Winnubst, Erhan Bas, Tiago A. Ferreira, Zhuhao Wu, Michael N Economo, Patrick Edson, Ben J. Arthur, Christopher Bruns, Konrad Rokicki, David Schauder, Donald J. Olbris, Sean D. Murphy, David G. Ackerman, Cameron Arshadi, Perry Baldwin, Regina Blake, Ahmad Elsayed, Mashtura Hasan, Daniel Ramirez, Bruno Dos Santos, Monet Weldon, Amina Zafar, Joshua T. Dudmann, Charles R. Gerfen, Adam W Hantman, Wyatt Korff, Scott M. Sternson, Nelson Spruston, Karel Svoboda, Jayaram Chandrashekar
Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and reveals how individual neurons route their output across the brain. Despite the importance of morphology, few projection neurons in the mouse brain have been reconstructed in their entirety. Here we present a robust and efficient platform for imaging and reconstructing complete neuronal morphologies, including axonal arbors that span substantial portions of the brain. We used this platform to reconstruct more than 1,000 projection neurons in the motor cortex, thalamus, subiculum, and hypothalamus. Together, the reconstructed neurons comprise more than 75 meters of axonal length and are available in a searchable online database. Axonal shapes revealed previously unknown subtypes of projection neurons and suggest organizational principles of long-range connectivity.
5,650 downloads neuroscience
We introduce a novel approach to study neurons as sophisticated I/O information processing units by utilizing recent advances in the field of machine learning. We trained deep neural networks (DNNs) to mimic the I/O behavior of a detailed nonlinear model of a layer 5 cortical pyramidal cell, receiving rich spatio-temporal patterns of input synapse activations. A Temporally Convolutional DNN (TCN) with seven layers was required to accurately, and very efficiently, capture the I/O of this neuron at the millisecond resolution. This complexity primarily arises from local NMDA-based nonlinear dendritic conductances. The weight matrices of the DNN provide new insights into the I/O function of cortical pyramidal neurons, and the approach presented can provide a systematic characterization of the functional complexity of different neuron types. Our results demonstrate that cortical neurons can be conceptualized as multi-layered “deep” processing units, implying that the cortical networks they form have a non-classical architecture and are potentially more computationally powerful than previously assumed.
5,632 downloads neuroscience
The hippocampal-entorhinal system is important for spatial and relational memory tasks. We formally link these domains; provide a mechanistic understanding of the hippocampal role in generalisation; and offer unifying principles underlying many entorhinal and hippocampal cell-types. We propose medial entorhinal cells form a basis describing structural knowledge, and hippocampal cells link this basis with sensory representations. Adopting these principles, we introduce the Tolman-Eichenbaum machine (TEM). After learning, TEM entorhinal cells include grid, band, border and object-vector cells. Hippocampal cells include place and landmark cells, remapping between environments. Crucially, TEM also predicts empirically recorded representations in complex non-spatial tasks. TEM predicts hippocampal remapping is not random as previously believed. Rather structural knowledge is preserved across environments. We confirm this in simultaneously recorded place and grid cells. One Sentence Summary Simple principles of representation and generalisation unify spatial and non-spatial accounts of hippocampus and explain many cell representations.
5,570 downloads 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.
5,506 downloads neuroscience
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.
5,448 downloads neuroscience
"Neural coding" is a popular metaphor in neuroscience, where objective properties of the world are communicated to the brain in the form of spikes. Here I argue that this metaphor is often inappropriate and misleading. First, when neurons are said to encode experimental parameters, the neural code depends on experimental details that are not carried by the coding variable. Thus, the representational power of neural codes is much more limited than generally implied. Second, neural codes carry information only by reference to things with known meaning. In contrast, perceptual systems must build information from relations between sensory signals and actions, forming a structured internal model. Neural codes are inadequate for this purpose because they are unstructured. Third, coding variables are observables tied to the temporality of experiments, while spikes are timed actions that mediate coupling in a distributed dynamical system. The coding metaphor tries to fit the dynamic, circular and distributed causal structure of the brain into a linear chain of transformations between observables, but the two causal structures are incongruent. I conclude that the neural coding metaphor cannot provide a basis for theories of brain function, because it is incompatible with both the causal structure of the brain and the informational requirements of cognition.
5,448 downloads neuroscience
When a neuron is driven beyond its threshold it spikes, and the fact that it does not communicate its continuous membrane potential is usually seen as a computational liability. Here we show that this spiking mechanism allows neurons to produce an unbiased estimate of their causal influence, and a way of approximating gradient descent learning. Importantly, neither activity of upstream neurons, which act as confounders, nor downstream non-linearities bias the results. By introducing a local discontinuity with respect to their input drive, we show how spiking enables neurons to solve causal estimation and learning problems.
5,439 downloads neuroscience
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.
5,421 downloads 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.
5,419 downloads 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.
5,407 downloads neuroscience
Single neurons in visual cortex provide unreliable measurements of visual features due to their high trial-to-trial variability. It is not known if this “noise” extends its effects over large neural populations to impair the global encoding of stimuli. We recorded simultaneously from ∼20,000 neurons in mouse primary visual cortex (V1) and found that the neural populations had discrimination thresholds of ∼0.34° in an orientation decoding task. These thresholds were nearly 100 times smaller than those reported behaviorally in mice. The discrepancy between neural and behavioral discrimination could not be explained by the types of stimuli we used, by behavioral states or by the sequential nature of perceptual learning tasks. Furthermore, higher-order visual areas lateral to V1 could be decoded equally well. These results imply that the limits of sensory perception in mice are not set by neural noise in sensory cortex, but by the limitations of downstream decoders.
5,386 downloads 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.
5,382 downloads neuroscience
Reconstruction of neural circuits from volume electron microscopy data requires the tracing of complete cells including all their neurites. Automated approaches have been developed to perform the tracing, but without costly human proofreading their error rates are too high to obtain reliable circuit diagrams. We present a method for automated segmentation that, like the majority of previous efforts, employs convolutional neural networks, but contains in addition a recurrent pathway that allows the iterative optimization and extension of the reconstructed shape of individual neural processes. We used this technique, which we call flood-filling networks, to trace neurons in a data set obtained by serial block-face electron microscopy from a male zebra finch brain. Our method achieved a mean error-free neurite path length of 1.1 mm, an order of magnitude better than previously published approaches applied to the same dataset. Only 4 mergers were observed in a neurite test set of 97 mm path length.
5,360 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.
5,359 downloads neuroscience
The curse of dimensionality plagues models of reinforcement learning and decision-making. The process of abstraction solves this by constructing abstract variables describing features shared by different specific instances, reducing dimensionality and enabling generalization in novel situations. Here we characterized neural representations in monkeys performing a task where a hidden variable described the temporal statistics of stimulus-response-outcome mappings. Abstraction was defined operationally using the generalization performance of neural decoders across task conditions not used for training. This type of generalization requires a particular geometric format of neural representations. Neural ensembles in dorsolateral pre-frontal cortex, anterior cingulate cortex and hippocampus, and in simulated neural networks, simultaneously represented multiple hidden and explicit variables in a format reflecting abstraction. Task events engaging cognitive operations modulated this format. These findings elucidate how the brain and artificial systems represent abstract variables, variables critical for generalization that in turn confers cognitive flexibility.
5,266 downloads 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.
5,259 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.
5,220 downloads neuroscience
A decade after the first successful attempt to decode speech directly from human brain signals, accuracy and speed remain far below that of natural speech or typing. Here we show how to achieve high accuracy from the electrocorticogram at natural-speech rates, even with few data (on the order of half an hour of spoken speech). Taking a cue from recent advances in machine translation and automatic speech recognition, we train a recurrent neural network to map neural signals directly to word sequences (sentences). In particular, the network first encodes a sentence-length sequence of neural activity into an abstract representation, and then decodes this representation, word by word, into an English sentence. For each participant, training data consist of several spoken repeats of a set of some 30-50 sentences, along with the corresponding neural signals at each of about 250 electrodes distributed over peri-Sylvian speech cortices. Average word error rates across a validation (held-out) sentence set are as low as 7% for some participants, as compared to the previous state of the art of greater than 60%. Finally, we show how to use transfer learning to overcome limitations on data availability: Training certain components of the network under multiple participants' data, while keeping other components (e.g., the first hidden layer) "proprietary," can improve decoding performance--despite very different electrode coverage across participants.
5,214 downloads 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.
5,209 downloads neuroscience
Despite the central role of sleep in our lives and the high prevalence of sleep disorders, sleep is still poorly understood. The development of ambulatory technologies capable of monitoring brain activity during sleep longitudinally is critical to advancing sleep science and facilitating the diagnosis of sleep disorders. We introduced the Dreem headband (DH) as an affordable, comfortable, and user-friendly alternative to polysomnography (PSG). The purpose of this study was to assess the signal acquisition of the DH and the performance of its embedded automatic sleep staging algorithms compared to the gold-standard clinical PSG scored by 5 sleep experts. Thirty-one subjects completed an over-night sleep study at a sleep center while wearing both a PSG and the DH simultaneously. We assessed 1) the EEG signal quality between the DH and the PSG, 2) the heart rate, breathing frequency, and respiration rate variability (RRV) agreement between the DH and the PSG, and 3) the performance of the DH's automatic sleep staging according to AASM guidelines vs. PSG sleep experts manual scoring. Results demonstrate a strong correlation between the EEG signals acquired by the DH and those from the PSG, and the signals acquired by the DH enable monitoring of alpha (r= 0.71 ± 0.13), beta (r= 0.71 ± 0.18), delta (r = 0.76 ± 0.14), and theta (r = 0.61 ± 0.12) frequencies during sleep. The mean absolute error for heart rate, breathing frequency and RRV was 1.2 ± 0.5 bpm, 0.3 ± 0.2 cpm and 3.2 ± 0.6%, respectively. Automatic Sleep Staging reached an overall accuracy of 83.5 ± 6.4% (F1 score : 83.8 ± 6.3) for the DH to be compared with an average of 86.4 ± 8.0% (F1 score: 86.3 ± 7.4) for the five sleep experts. These results demonstrate the capacity of the DH to both precisely monitor sleep-related physiological signals and process them accurately into sleep stages. This device paves the way for high-quality, large-scale, longitudinal sleep studies.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!