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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 62,963 bioRxiv papers from 279,321 authors.

Most downloaded bioRxiv papers, all time

in category neuroscience

10,948 results found. For more information, click each entry to expand.

81: Modulation of Body Mass Composition using Vestibular Nerve Stimulation
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Posted to bioRxiv 14 Nov 2016

Modulation of Body Mass Composition using Vestibular Nerve Stimulation
3,843 downloads neuroscience

Paul D. McGeoch, Jason McKeown, Hans Peterson, V.S. Ramachandran

There is increasing evidence of a set-point for body weight in the brain, that is regulated by the hypothalamus. This system modifies feeding behavior and metabolic rate, to keep body fat within predetermined parameters. It is also known that animals subjected to chronic centrifugation show a reduction in body fat. Experiments with mutant mice found that this loss of fat appears to be mediated by a vestibulo-hypothalamic pathway. Vestibular nerve stimulation (VeNS), also known as galvanic vestibular stimulation, involves non-invasively stimulating the vestibular system by applying a small electrical current between two electrodes placed over the mastoid processes. We suggest that any means of repeatedly stimulating the otolith organs in humans would cause a reduction in total body fat, and that VeNS would be a useful technique to use in this regard. Below we provide pilot data to support this idea.

82: Optimizing Real Time fMRI Neurofeedback for Therapeutic Discovery and Development
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Posted to bioRxiv 18 Mar 2014

Optimizing Real Time fMRI Neurofeedback for Therapeutic Discovery and Development
3,814 downloads neuroscience

L. E. Stoeckel, K. A. Garrison, S. Ghosh, P. Wighton, C. A. Hanlon, J. M. Gilman, S. Greer, N. B. Turk-Browne, M. T. deBettencourt, D. Scheinost, C. Craddock, T. Thompson, V. Calderon, C. C. Bauer, M. George, H. C. Breiter, S. Whitfield-Gabrieli, J. D. Gabrieli, S.M. LaConte, L. Hirshberg, J. A. Brewer, M. Hampson, A. Van Der Kouwe, S. Mackey, A. E. Evins

While reducing the burden of brain disorders remains a top priority of organizations like the World Health Organization and National Institutes of Health (BRAIN, 2013), the development of novel, safe and effective treatments for brain disorders has been slow. In this paper, we describe the state of the science for an emerging technology, real time functional magnetic resonance imaging (rtfMRI) neurofeedback, in clinical neurotherapeutics. We review the scientific potential of rtfMRI and outline research strategies to optimize the development and application of rtfMRI neurofeedback as a next generation therapeutic tool. We propose that rtfMRI can be used to address a broad range of clinical problems by improving our understanding of brain-behavior relationships in order to develop more specific and effective interventions for individuals with brain disorders. We focus on the use of rtfMRI neurofeedback as a clinical neurotherapeutic tool to drive plasticity in brain function, cognition, and behavior. Our overall goal is for rtfMRI to advance personalized assessment and intervention approaches to enhance resilience and reduce morbidity by correcting maladaptive patterns of brain function in those with brain disorders.

83: Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?
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Posted to bioRxiv 05 Sep 2018

Brain-Score: Which Artificial Neural Network for Object Recognition is most Brain-Like?
3,814 downloads neuroscience

Martin Schrimpf, Jonas Kubilius, Ha Hong, Najib J. Majaj, Rishi Rajalingham, Elias B. Issa, Kohitij Kar, Pouya Bashivan, Jonathan Prescott-Roy, Kailyn Schmidt, Daniel L. K. Yamins, James J DiCarlo

The internal representations of early deep artificial neural networks (ANNs) were found to be remarkably similar to the internal neural representations measured experimentally in the primate brain. Here we ask, as deep ANNs have continued to evolve, are they becoming more or less brain-like? ANNs that are most functionally similar to the brain will contain mechanisms that are most like those used by the brain. We therefore developed Brain-Score - a composite of multiple neural and behavioral benchmarks that score any ANN on how similar it is to the brain's mechanisms for core object recognition - and we deployed it to evaluate a wide range of state-of-the-art deep ANNs. Using this scoring system, we here report that: (1) DenseNet-169, CORnet-S and ResNet-101 are the most brain-like ANNs. (2) There remains considerable variability in neural and behavioral responses that is not predicted by any ANN, suggesting that no ANN model has yet captured all the relevant mechanisms. (3) Extending prior work, we found that gains in ANN ImageNet performance led to gains on Brain-Score. However, correlation weakened at >= 70% top-1 ImageNet performance, suggesting that additional guidance from neuroscience is needed to make further advances in capturing brain mechanisms. (4) We uncovered smaller (i.e. less complex) ANNs that are more brain-like than many of the best-performing ImageNet models, which suggests the opportunity to simplify ANNs to better understand the ventral stream. The scoring system used here is far from complete. However, we propose that evaluating and tracking model-benchmark correspondences through a Brain-Score that is regularly updated with new brain data is an exciting opportunity: experimental benchmarks can be used to guide machine network evolution, and machine networks are mechanistic hypotheses of the brain's network and thus drive next experiments. To facilitate both of these, we release Brain-Score.org: a platform that hosts the neural and behavioral benchmarks, where ANNs for visual processing can be submitted to receive a Brain-Score and their rank relative to other models, and where new experimental data can be naturally incorporated.

84: A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging
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Posted to bioRxiv 30 May 2016

A large field of view two-photon mesoscope with subcellular resolution for in vivo imaging
3,811 downloads neuroscience

N. J. Sofroniew, D. Flickinger, J. King, K Svoboda

Imaging is used to map activity across populations of neurons. Microscopes with cellular resolution have small (< 1 millimeter) fields of view and cannot simultaneously image activity distributed across multiple brain areas. Typical large field of view microscopes do not resolve single cells, especially in the axial dimension. We developed a 2-photon random access mesoscope (2p-RAM) that allows high-resolution imaging anywhere within a volume spanning multiple brain areas (∅ 5 mm x 1 mm cylinder). 2p-RAM resolution is near diffraction limited (lateral, 0.66 μm, axial 4.09 μm at the center; excitation wavelength = 970 nm; numerical aperture = 0.6) over a large range of excitation wavelengths. A fast three-dimensional scanning system allows efficient sampling of neural activity in arbitrary regions of interest across the entire imaging volume. We illustrate the use of the 2p-RAM by imaging neural activity in multiple, non-contiguous brain areas in transgenic mice expressing protein calcium sensors.

85: When null hypothesis significance testing is unsuitable for research: a reassessment
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Posted to bioRxiv 20 Dec 2016

When null hypothesis significance testing is unsuitable for research: a reassessment
3,808 downloads neuroscience

Denes Szucs, John PA Ioannidis

Null hypothesis significance testing (NHST) has several shortcomings that are likely contributing factors behind the widely debated replication crisis of psychology, cognitive neuroscience and biomedical science in general. We review these shortcomings and suggest that, after about 60 years of negative experience, NHST should no longer be the default, dominant statistical practice of all biomedical and psychological research. Different inferential methods (NHST, likelihood estimation, Bayesian methods, false-discovery rate control) may be most suitable for different types of research questions. Whenever researchers use NHST they should justify its use, and publish pre-study power calculations and effect sizes, including negative findings. Studies should optimally be pre-registered and raw data published. The current statistics lite educational approach for students that has sustained the widespread, spurious use of NHST should be phased out. Instead, we should encourage either more in-depth statistical training of more researchers and/or more widespread involvement of professional statisticians in all research.

86: Towards reconstructing intelligible speech from the human auditory cortex
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Posted to bioRxiv 19 Jun 2018

Towards reconstructing intelligible speech from the human auditory cortex
3,800 downloads neuroscience

Hassan Akbari, Bahar Khalighinejad, Jose L Herrero, Ashesh D. Mehta, Nima Mesgarani

Auditory stimulus reconstruction is a technique that finds the best approximation of the acoustic stimulus from the population of evoked neural activity. Reconstructing speech from the human auditory cortex creates the possibility of a speech neuroprosthetic to establish a direct communication with the brain and has been shown to be possible in both overt and covert conditions. However, the low quality of the reconstructed speech has severely limited the utility of this method for brain-computer interface (BCI) applications. To advance the state-of-the-art in speech neuroprosthesis, we combined the recent advances in deep learning with the latest innovations in speech synthesis technologies to reconstruct closed-set intelligible speech from the human auditory cortex. We investigated the dependence of reconstruction accuracy on linear and nonlinear regression methods and the acoustic representation that is used as the target of reconstruction, including spectrogram and speech synthesis parameters. In addition, we compared the reconstruction accuracy from low and high neural frequency ranges. Our results show that a deep neural network model that directly estimates the parameters of a speech synthesizer from all neural frequencies achieves the highest subjective and objective scores on a digit recognition task, improving the intelligibility by 65% over the baseline. These results demonstrate the efficacy of deep learning and speech synthesis algorithms for designing the next generation of speech BCI systems, which not only can restore communications for paralyzed patients but also have the potential to transform human-computer interaction technologies.

87: Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction
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Posted to bioRxiv 19 Jan 2017

Real-time spike sorting platform for high-density extracellular probes with ground-truth validation and drift correction
3,792 downloads neuroscience

James J. Jun, Catalin Mitelut, Chongxi Lai, Sergey L Gratiy, Costas A Anastassiou, Timothy D Harris

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.

88: Automated analysis of whole brain vasculature using machine learning
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Posted to bioRxiv 18 Apr 2019

Automated analysis of whole brain vasculature using machine learning
3,787 downloads neuroscience

Mihail Ivilinov Todorov, Johannes C. Paetzold, Oliver Schoppe, Giles Tetteh, Velizar Efremov, Katalin Völgyi, Marco Düring, Martin Dichgans, Marie Piraud, Bjoern Menze, Ali Ertürk

Tissue clearing methods enable imaging of intact biological specimens without sectioning. However, reliable and scalable analysis of such large imaging data in 3D remains a challenge. Towards this goal, we developed a deep learning-based framework to quantify and analyze the brain vasculature, named Vessel Segmentation & Analysis Pipeline (VesSAP). Our pipeline uses a fully convolutional network with a transfer learning approach for segmentation. We systematically analyzed vascular features of the whole brains including their length, bifurcation points and radius at the micrometer scale by registering them to the Allen mouse brain atlas. We reported the first evidence of secondary intracranial collateral vascularization in CD1-Elite mice and found reduced vascularization in the brainstem as compared to the cerebrum. VesSAP thus enables unbiased and scalable quantifications for the angioarchitecture of the cleared intact mouse brain and yields new biological insights related to the vascular brain function.

89: Predictive Coding of Novel versus Familiar Stimuli in the Primary Visual Cortex
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Posted to bioRxiv 03 Oct 2017

Predictive Coding of Novel versus Familiar Stimuli in the Primary Visual Cortex
3,770 downloads neuroscience

Jan Homann, Sue Ann Koay, Alistair M. Glidden, David W Tank, Michael J Berry

To explore theories of predictive coding, we presented mice with repeated sequences of images with novel im- ages sparsely substituted. Under these conditions, mice could be rapidly trained to lick in response to a novel image, demonstrating a high level of performance on the first day of testing. Using 2-photon calcium imaging to record from layer 2/3 neurons in the primary visual cor- tex, we found that novel images evoked excess activity in the majority of neurons. When a new stimulus se- quence was repeatedly presented, a majority of neurons had similarly elevated activity for the first few presenta- tions, which then decayed to almost zero activity. The decay time of these transient responses was not fixed, but instead scaled with the length of the stimulus sequence. However, at the same time, we also found a small fraction of the neurons within the population (~2%) that contin- ued to respond strongly and periodically to the repeated stimulus. Decoding analysis demonstrated that both the transient and sustained responses encoded information about stimulus identity. We conclude that the layer 2/3 population uses a two-channel predictive code: a dense transient code for novel stimuli and a sparse sustained code for familiar stimuli. These results extend and unify existing theories about the nature of predictive neural codes.

90: Motor cortex is an input-driven dynamical system controlling dexterous movement
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Posted to bioRxiv 15 Feb 2018

Motor cortex is an input-driven dynamical system controlling dexterous movement
3,766 downloads neuroscience

Britton Sauerbrei, Jian-Zhong Guo, Matteo Mischiati, Wendy Guo, Mayank Kabra, Nakul Verma, Kristin Branson, Adam Hantman

Skillful control of movement is central to our ability to sense and manipulate the world. A large body of work in nonhuman primates has demonstrated that motor cortex provides flexible, time-varying activity patterns that control the arm during reaching and grasping. Previous studies have suggested that these patterns are generated by strong local recurrent dynamics operating autonomously from inputs during movement execution. An alternative possibility is that motor cortex requires coordination with upstream brain regions throughout the entire movement in order to yield these patterns. Here, we developed an experimental preparation in the mouse to directly test these possibilities using optogenetics and electrophysiology during a skilled reach-to-grab-to-eat task. To validate this preparation, we first established that a specific, time-varying pattern of motor cortical activity was required to produce coordinated movement. Next, in order to disentangle the contribution of local recurrent motor cortical dynamics from external input, we optogenetically held the recurrent contribution constant, then observed how motor cortical activity recovered following the end of this perturbation. Both the neural responses and hand trajectory varied from trial to trial, and this variability reflected variability in external inputs. To directly probe the role of these inputs, we used optogenetics to perturb activity in the thalamus. Thalamic perturbation at the start of the trial prevented movement initiation, and perturbation at any stage of the movement prevented progression of the hand to the target; this demonstrates that input is required throughout the movement. By comparing motor cortical activity with and without thalamic perturbation, we were able to estimate the effects of external inputs on motor cortical population activity. Thus, unlike pattern-generating circuits that are local and autonomous, such as those in the spinal cord that generate left-right alternation during locomotion, the pattern generator for reaching and grasping is distributed across multiple, strongly-interacting brain regions.

91: Temporal Evolution of Cortical Ensembles Promoting Remote Memory Retrieval
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Posted to bioRxiv 05 Apr 2018

Temporal Evolution of Cortical Ensembles Promoting Remote Memory Retrieval
3,766 downloads neuroscience

Laura A DeNardo, Cindy D Liu, William E Allen, Eliza L Adams, Drew Friedmann, Ehsan Dadgar-Kiani, Lisa Fu, Casey J Guenthner, Jin Hyung Lee, Marc Tessier-Lavigne, Liqun Luo

Studies of amnesic patients and animal models support a systems consolidation model, which posits that explicit memories formed in hippocampus are transferred to cortex over time1-6. Prelimbic cortex (PL), a subregion of the medial prefrontal cortex, is required for the expression of learned fear memories from hours after learning until weeks later7-12. While some studies suggested that prefrontal cortical neurons active during learning are required for memory retrieval13-15, others provided evidence for ongoing cortical circuit reorganization during memory consolidation10,16,17. It has been difficult to causally relate the activity of cortical neurons during learning or recent memory retrieval to their function in remote memory, in part due to a lack of tools18. Here we show that a new version of 'targeted recombination in active populations', TRAP2, has enhanced efficiency over the past version, providing brain-wide access to neurons activated by a particular experience. Using TRAP2, we accessed PL neurons activated during fear conditioning or 1-, 7-, or 14-day memory retrieval, and assessed their contributions to 28-day remote memory. We found that PL neurons TRAPed at later retrieval times were more likely to be reactivated during remote memory retrieval, and more effectively promoted remote memory retrieval. Furthermore, reducing PL activity during learning blunted the ability of TRAPed PL neurons to promote remote memory retrieval. Finally, a series of whole-brain analyses identified a set of cortical regions that were densely innervated by memory-TRAPed PL neurons and preferentially activated by PL neurons TRAPed during 14-day retrieval, and whose activity co-varied with PL and correlated with memory specificity. These findings support a model in which PL ensembles underlying remote memory undergo dynamic changes during the first two weeks after learning, which manifest as increased functional recruitment of cortical targets.

92: Probing variability in a cognitive map using manifold inference from neural dynamics
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Posted to bioRxiv 16 Sep 2018

Probing variability in a cognitive map using manifold inference from neural dynamics
3,761 downloads neuroscience

Ryan J Low, Sam Lewallen, Dmitriy Aronov, Rhino Nevers, David W Tank

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.

93: CaImAn: An open source tool for scalable Calcium Imaging data Analysis
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Posted to bioRxiv 05 Jun 2018

CaImAn: An open source tool for scalable Calcium Imaging data Analysis
3,756 downloads neuroscience

Andrea Giovannucci, Johannes Friedrich, Pat Gunn, Jeremie Kalfon, Sue Ann Koay, Jiannis Taxidis, Farzaneh Najafi, Jeffrey L Gauthier, Pengcheng Zhou, David W Tank, Dmitri Chklovskii, Eftychios A Pnevmatikakis

Advances in fluorescence microscopy enable monitoring larger brain areas in-vivo with finer time resolution. The resulting data rates require reproducible analysis pipelines that are reliable, fully automated, and scalable to datasets generated over the course of months. Here we present CaImAn, an open-source library for calcium imaging data analysis. CaImAn provides automatic and scalable methods to address problems common to pre-processing, including motion correction, neural activity identification, and registration across different sessions of data collection. It does this while requiring minimal user intervention, with good performance on computers ranging from laptops to high-performance computing clusters. CaImAn is suitable for two-photon and one-photon imaging, and also enables real-time analysis on streaming data. To benchmark the performance of CaImAn, we collected a corpus of ground truth annotations from multiple labelers on nine mouse two-photon datasets. We demonstrate that CaImAn achieves near-human performance in detecting locations of active neurons.

94: Crossvalidation in brain imaging analysis
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Posted to bioRxiv 01 Apr 2015

Crossvalidation in brain imaging analysis
3,723 downloads neuroscience

Nikolaus Kriegeskorte

Crossvalidation is a method for estimating predictive performance and adjudicating between multiple models. On each of k folds of the process, k-1 of k independent subsets of the data (training set) are used to fit the parameters of each model and the left-out subset (test set) is used to estimate predictive performance. The method is statistically efficient, because training data are reused for testing and performance estimates combined across folds. The method requires no assumptions, provides nearly unbiased (slightly conservative) estimates of predictive performance, and is generally applicable because it amounts to a direct empirical test of each model.

95: Sensitive red protein calcium indicators for imaging neural activity
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Posted to bioRxiv 29 Feb 2016

Sensitive red protein calcium indicators for imaging neural activity
3,681 downloads neuroscience

Hod Dana, Boaz Mohar, Yi Sun, Sujatha Narayan, Andrew Gordus, Jeremy P Hasseman, Getahun Tsegaye, Graham T Holt, Amy Hu, Deepika Walpita, Ronak Patel, John J Macklin, Cornelia I Bargmann, Misha B Ahrens, Eric R Schreiter, Vivek Jayaraman, Loren L Looger, Karel Svoboda, Douglas S Kim

Genetically encoded calcium indicators (GECIs) allow measurement of activity in large populations of neurons and in small neuronal compartments, over times of milliseconds to months. Although GFP-based GECIs are widely used for in vivo neurophysiology, GECIs with red-shifted excitation and emission spectra have advantages for in vivo imaging because of reduced scattering and absorption in tissue, and a consequent reduction in phototoxicity. However, current red GECIs are inferior to the state-of-the-art GFP-based GCaMP6 indicators for detecting and quantifying neural activity. Here we present improved red GECIs based on mRuby (jRCaMP1a, b) and mApple (jRGECO1a), with sensitivity comparable to GCaMP6. We characterized the performance of the new red GECIs in cultured neurons and in mouse, Drosophila, zebrafish and C. elegans in vivo. Red GECIs facilitate deep-tissue imaging, dual-color imaging together with GFP-based reporters, and the use of optogenetics in combination with calcium imaging.

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Posted to bioRxiv 03 Oct 2016

3,665 downloads neuroscience

Michael Skocik, John Collins, Chloe Callahan-Flintoft, Howard Bowman, Brad Wyble

Machine learning is a powerful set of techniques that has enhanced the abilities of neuroscientists to interpret information collected through EEG, fMRI, MEG, and PET data. With these new techniques come new dangers of overfitting that are not well understood by the neuroscience community. In this article, we use Support Vector Machine (SVM) classifiers, and genetic algorithms to demonstrate the ease by which overfitting can occur, despite the use of cross validation. We demonstrate that comparable and non-generalizable results can be obtained on informative and non-informative (i.e. random) data by iteratively modifying hyperparameters in seemingly innocuous ways. We recommend a number of techniques for limiting overfitting, such as lock boxes, blind analyses, and pre-registrations. These techniques, although uncommon in neuroscience applications, are common in many other fields that use machine learning, including computer science and physics. Adopting similar safeguards is critical for ensuring the robustness of machine-learning techniques.

97: Classification of electrophysiological and morphological types in mouse visual cortex
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Posted to bioRxiv 17 Jul 2018

Classification of electrophysiological and morphological types in mouse visual cortex
3,663 downloads neuroscience

Nathan W. Gouwens, Staci A. Sorensen, Jim Berg, Changkyu Lee, Tim Jarsky, Jonathan Ting, Susan M Sunkin, David Feng, Costas Anastassiou, Eliza Barkan, Kris Bickley, Nicole Blesie, Thomas Braun, Krissy Brouner, Agata Budzillo, Shiella Caldejon, Tamara Casper, Dan Casteli, Peter Chong, Kirsten Crichton, Christine Cuhaciyan, Tanya Daigle, Rachel Dalley, Nick Dee, Tsega Desta, Samuel Dingman, Alyse Doperalski, Nadezhda Dotson, Tom Egdorf, Michael Fisher, Rebecca A de Frates, Emma Garren, Marissa Garwood, Amanda Gary, Nathalie Gaudreault, Keith Godfrey, Melissa Gorham, Thuc Nghi Nguyen, Caroline Habel, Kristen Hadley, James Harrington, Julie Harris, Alex Henry, DiJon Hill, Sam Josephsen, Sara Kebede, Lisa Kim, Matthew Kroll, Brian Lee, Tracy Lemon, Xiaoxiao Liu, Brian Long, Rusty Mann, Medea McGraw, Stefan Mihalas, Alice Mukora, Gabe J Murphy, Lindsay Ng, Kiet Ngo, Philip R Nicovich, Aaron Oldre, Daniel Park, Sheana Parry, Jed Perkins, Lydia Potekhina, David Reid, Miranda Robertson, David Sandman, Martin Schroedter, Cliff Slaughterbeck, Gilberto Soler-Llavina, Josef Sulc, Aaron Szafer, Bosiljka Tasic, Naz Taskin, Corinne Teeter, Nivretta Thatra, Herman Tung, Wayne Wakeman, Grace Williams, Rob Young, Zhi Zhou, Colin Farrell, Hanchuan Peng, Michael J Hawrylycz, Ed Lein, Lydia Ng, Anton Arkhipov, Amy Bernard, John W Phillips, Hongkui Zeng, Christof Koch

Understanding the diversity of cell types in the brain has been an enduring challenge and requires detailed characterization of individual neurons in multiple dimensions. To profile morpho-electric properties of mammalian neurons systematically, we established a single cell characterization pipeline using standardized patch clamp recordings in brain slices and biocytin-based neuronal reconstructions. We built a publicly-accessible online database, the Allen Cell Types Database, to display these data sets. Intrinsic physiological and morphological properties were measured from over 1,800 neurons from the adult laboratory mouse visual cortex. Quantitative features were used to classify neurons into distinct types using unsupervised methods. We establish a taxonomy of morphologically- and electrophysiologically-defined cell types for this region of cortex with 17 e-types and 35 m-types, as well as an initial correspondence with previously-defined transcriptomic cell types using the same transgenic mouse lines.

98: Transdermal neuromodulation of noradrenergic activity suppresses psychophysiological and biochemical stress responses in humans
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Posted to bioRxiv 08 Feb 2015

Transdermal neuromodulation of noradrenergic activity suppresses psychophysiological and biochemical stress responses in humans
3,663 downloads neuroscience

William J. Tyler, Alyssa M. Boasso, Hailey M. Mortimore, Rhonda S. Silva, Jonathan D. Charlesworth, Michelle A. Marlin, Kirsten Aebersold, Linh Aven, Daniel Z. Wetmore, Sumon K. Pal

We engineered a transdermal neuromodulation approach that targets peripheral (cranial and spinal) nerves and utilizes their afferent pathways as signaling conduits to influence brain function. We investigated the effects of this transdermal electrical neurosignaling (TEN) method on sympathetic physiology in human volunteers under different experimental conditions. In all cases, the TEN involved delivering high-frequency pulsed electrical currents to ophthalmic and maxillary divisions of the right trigeminal nerve (V1/V2) and cervical spinal nerve afferents (C2/C3). Under resting conditions when subjects were not challenged or presented with environmental stimuli, TEN significantly suppressed basal sympathetic tone compared to sham as indicated by functional infrared thermography of facial temperatures. In a different experiment conducted under similar resting conditions, subjects treated with TEN reported significantly lower levels of tension and anxiety on the Profile of Mood States scale compared to sham. In a third experiment when subjects were experimentally stressed by a classical fear conditioning paradigm and a series of time-constrained cognitive tasks, TEN produced a significant suppression of heart rate variability, galvanic skin conductance, and salivary α-amylase levels compared to sham. Collectively these observations demonstrate TEN can dampen basal sympathetic tone and attenuate sympathetic activity in response to acute stress induction. Our physiological and biochemical observations are consistent with the hypothesis that TEN modulates noradrenergic signaling to suppress sympathetic activity. We conclude that dampening sympathetic activity in such a manner represents a promising approach to managing daily stress.

99: Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis
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Posted to bioRxiv 30 Oct 2017

Unsupervised discovery of demixed, low-dimensional neural dynamics across multiple timescales through tensor components analysis
3,605 downloads neuroscience

Alex H Williams, Tony Hyun Kim, Forea Wang, Saurabh Vyas, Stephen I Ryu, Krishna V Shenoy, Mark Schnitzer, Tamara G Kolda, Surya Ganguli

Perceptions, thoughts and actions unfold over millisecond timescales, while learned behaviors can require many days to mature. While recent experimental advances enable large-scale and long-term neural recordings with high temporal fidelity, it remains a formidable challenge to extract unbiased and interpretable descriptions of how rapid single-trial circuit dynamics change slowly over many trials to mediate learning. We demonstrate a simple tensor components analysis (TCA) can meet this challenge by extracting three interconnected low dimensional descriptions of neural data: neuron factors, reflecting cell assemblies; temporal factors, reflecting rapid circuit dynamics mediating perceptions, thoughts, and actions within each trial; and trial factors, describing both long-term learning and trial-to-trial changes in cognitive state. We demonstrate the broad applicability of TCA by revealing insights into diverse datasets derived from artificial neural networks, large-scale calcium imaging of rodent prefrontal cortex during maze navigation, and multielectrode recordings of macaque motor cortex during brain machine interface learning.

100: A self-initiated two-alternative forced choice paradigm for head-fixed mice
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Posted to bioRxiv 08 Sep 2016

A self-initiated two-alternative forced choice paradigm for head-fixed mice
3,582 downloads neuroscience

Fred Marbach, Anthony M Zador

Psychophysical tasks for non-human primates have been instrumental in studying circuits underlying perceptual decision-making. To obtain greater experimental flexibility, these tasks have subsequently been adapted for use in freely moving rodents. However, advances in functional imaging and genetic targeting of neuronal populations have made it critical to develop similar tasks for head-fixed mice. Although head-fixed mice have been trained in two-alternative forced choice tasks before, these tasks were not self-initiated, making it difficult to attribute error trials to perceptual or decision errors as opposed to mere lapses in task engagement. Here, we describe a paradigm for head-fixed mice with three lick spouts, analogous to the well-established 3-port paradigm for freely moving rodents. Mice readily learned to initiate trials on the center spout and performed around 200 self-initiated trials per session, reaching good psychometric performance within two weeks of training. We expect this paradigm will be useful to study the role of defined neural populations in sensory processing and decision-making.

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