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1339 Janelia Publications

Showing 71-80 of 1339 results
12/09/17 | Optogenetic dissection of descending behavioral control in Drosophila.
Cande J, Berman GJ, Namiki S, Qiu J, Korff W, Card GM, Shaevitz JW, Stern DL
bioRxiv. 2017 Dec 9:230128. doi: 10.1101/230128

In most animals, the brain makes behavioral decisions that are transmitted by descending neurons to the nerve cord circuitry that produces behaviors. In insects, only a few descending neurons have been associated with specific behaviors. To explore how descending neurons control insect behavior, we developed a novel method to systematically assay the behavioral effects of 160 descending neurons in freely behaving terrestrial D. melanogaster using optogenetic activation. We calculated a 2-dimensional representation of the entire behavior space explored by these flies and associated descending neurons with specific behaviors by identifying regions of this space that were visited with increased frequency during optogenetic activation. We found, that (1) activation of most of the descending neurons drove stereotyped behaviors, (2) in many cases multiple descending neurons activated similar behaviors, and (3) optogenetically-activated behaviors were often dependent on the behavioral state prior to activation.

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12/04/17 | Extracting low-dimensional dynamics from multiple large-scale neural population recordings by learning to predict correlations.
Nonnenmacher M, Turaga SC, Macke JH
31st Conference on Neural Information Processing Systems (NIPS 2017). 2017 Dec 04:

A powerful approach for understanding neural population dynamics is to extract low-dimensional trajectories from population recordings using dimensionality reduction methods. Current approaches for dimensionality reduction on neural data are limited to single population recordings, and can not identify dynamics embedded across multiple measurements. We propose an approach for extracting low-dimensional dynamics from multiple, sequential recordings. Our algorithm scales to data comprising millions of observed dimensions, making it possible to access dynamics distributed across large populations or multiple brain areas. Building on subspace-identification approaches for dynamical systems, we perform parameter estimation by minimizing a moment-matching objective using a scalable stochastic gradient descent algorithm: The model is optimized to predict temporal covariations across neurons and across time. We show how this approach naturally handles missing data and multiple partial recordings, and can identify dynamics and predict correlations even in the presence of severe subsampling and small overlap between recordings. We demonstrate the effectiveness of the approach both on simulated data and a whole-brain larval zebrafish imaging dataset. 

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12/04/17 | Fast amortized inference of neural activity from calcium imaging data with variational autoencoders.
Speiser A, Yan J, Archer E, Buesing L, Turaga SC, Macke JH
Neural Information Processing Systems (NIPS 2017). 2017 Dec 04:

Calcium imaging permits optical measurement of neural activity. Since intracellular calcium concentration is an indirect measurement of neural activity, computational tools are necessary to infer the true underlying spiking activity from fluorescence measurements. Bayesian model inversion can be used to solve this problem, but typically requires either computationally expensive MCMC sampling, or faster but approximate maximum-a-posteriori optimization. Here, we introduce a flexible algorithmic framework for fast, efficient and accurate extraction of neural spikes from imaging data. Using the framework of variational autoencoders, we propose to amortize inference by training a deep neural network to perform model inversion efficiently. The recognition network is trained to produce samples from the posterior distribution over spike trains. Once trained, performing inference amounts to a fast single forward pass through the network, without the need for iterative optimization or sampling. We show that amortization can be applied flexibly to a wide range of nonlinear generative models and significantly improves upon the state of the art in computation time, while achieving competitive accuracy. Our framework is also able to represent posterior distributions over spike-trains. We demonstrate the generality of our method by proposing the first probabilistic approach for separating backpropagating action potentials from putative synaptic inputs in calcium imaging of dendritic spines. 

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12/04/17 | Model-based Bayesian inference of neural activity and connectivity from all-optical interrogation of a neural circuit.
Aitchison L, Russell L, Packer AM, Yan J, Castonguaye P, Häusser M, Turaga SC
31st Conference on Neural Information Processing Systems (NIPS 2017). 2017 Dec 04:

Population activity measurement by calcium imaging can be combined with cellular resolution optogenetic activity perturbations to enable the mapping of neural connectivity in vivo. This requires accurate inference of perturbed and unperturbed neural activity from calcium imaging measurements, which are noisy and indirect, and can also be contaminated by photostimulation artifacts. We have developed a new fully Bayesian approach to jointly inferring spiking activity and neural connectivity from in vivo all-optical perturbation experiments. In contrast to standard approaches that perform spike inference and analysis in two separate maximum-likelihood phases, our joint model is able to propagate uncertainty in spike inference to the inference of connectivity and vice versa. We use the framework of variational autoencoders to model spiking activity using discrete latent variables, low-dimensional latent common input, and sparse spike-and-slab generalized linear coupling between neurons. Additionally, we model two properties of the optogenetic perturbation: off-target photostimulation and photostimulation transients. Our joint model includes at least two sets of discrete random variables; to avoid the dramatic slowdown typically caused by being unable to differentiate such variables, we introduce two strategies that have not, to our knowledge, been used with variational autoencoders. Using this model, we were able to fit models on 30 minutes of data in just 10 minutes. We performed an all-optical circuit mapping experiment in primary visual cortex of the awake mouse, and use our approach to predict neural connectivity between excitatory neurons in layer 2/3. Predicted connectivity is sparse and consistent with known correlations with stimulus tuning, spontaneous correlation and distance.

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12/04/17 | Visualizing long-term single-molecule dynamics in vivo by stochastic protein labeling.
Liu H, Dong P, Ioannou MS, Li L, Shea J, Pasolli HA, Grimm JB, Rivlin PK, Lavis LD, Koyama M, Liu Z
Proceedings of the National Academy of Sciences of the United States of America. 2017 Jan 09;115(2):343-8. doi: 10.1073/pnas.1713895115

Our ability to unambiguously image and track individual molecules in live cells is limited by packing of multiple copies of labeled molecules within the resolution limit. Here we devise a universal genetic strategy to precisely control copy number of fluorescently labeled molecules in a cell. This system has a dynamic titration range of >10,000 fold, enabling sparse labeling of proteins expressed at different abundance levels. Combined with photostable labels, this system extends the duration of automated single-molecule tracking by 2 orders of magnitude. We demonstrate long-term imaging of synaptic vesicle dynamics in cultured neurons as well as in intact zebrafish. We found axon initial segment utilizes a "waterfall" mechanism gating synaptic vesicle transport polarity by promoting anterograde transport processivity. Long-time observation also reveals that transcription factor hops between clustered binding sites in spatially-restricted sub-nuclear regions, suggesting that topological structures in the nucleus shape local gene activities by a sequestering mechanism. This strategy thus greatly expands the spatiotemporal length scales of live-cell single-molecule measurements, enabling new experiments to quantitatively understand complex control of molecular dynamics in vivo.

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12/01/17 | Editorial overview: Making evolutionary sense of everything.
Stern DL, Haag E
Current Opinion in Genetics & Development. 2017 Dec;47:iv-vi. doi: 10.1016/j.gde.2017.11.005
11/20/17 | Neural mechanisms of movement planning: motor cortex and beyond.
Svoboda K, Li N
Current Opinion in Neurobiology. 2017 Nov 20;49:33-41. doi: 10.1016/j.conb.2017.10.023

Neurons in motor cortex and connected brain regions fire in anticipation of specific movements, long before movement occurs. This neural activity reflects internal processes by which the brain plans and executes volitional movements. The study of motor planning offers an opportunity to understand how the structure and dynamics of neural circuits support persistent internal states and how these states influence behavior. Recent advances in large-scale neural recordings are beginning to decipher the relationship of the dynamics of populations of neurons during motor planning and movements. New behavioral tasks in rodents, together with quantified perturbations, link dynamics in specific nodes of neural circuits to behavior. These studies reveal a neural network distributed across multiple brain regions that collectively supports motor planning. We review recent advances and highlight areas where further work is needed to achieve a deeper understanding of the mechanisms underlying motor planning and related cognitive processes.

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11/20/17 | Structure-based inhibitors of tau aggregation.
Seidler PM, Boyer DR, Rodriguez JA, Sawaya MR, Cascio D, Murray K, Gonen T, Eisenberg DS
Nature Chemistry. 2017 Nov 20:. doi: 10.1038/nchem.2889

Aggregated tau protein is associated with over 20 neurological disorders, which include Alzheimer's disease. Previous work has shown that tau's sequence segments VQIINK and VQIVYK drive its aggregation, but inhibitors based on the structure of the VQIVYK segment only partially inhibit full-length tau aggregation and are ineffective at inhibiting seeding by full-length fibrils. Here we show that the VQIINK segment is the more powerful driver of tau aggregation. Two structures of this segment determined by the cryo-electron microscopy method micro-electron diffraction explain its dominant influence on tau aggregation. Of practical significance, the structures lead to the design of inhibitors that not only inhibit tau aggregation but also inhibit the ability of exogenous full-length tau fibrils to seed intracellular tau in HEK293 biosensor cells into amyloid. We also raise the possibility that the two VQIINK structures represent amyloid polymorphs of tau that may account for a subset of prion-like strains of tau.

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11/17/17 | Structural basis of bacterial transcription activation.
Liu B, Hong C, Huang RK, Yu Z, Steitz TA
Science (New York, N.Y.). 2017 Nov 17;358(6365):947-951. doi: 10.1126/science.aao1923

In bacteria, the activation of gene transcription at many promoters is simple and only involves a single activator. The cyclic adenosine 3',5'-monophosphate receptor protein (CAP), a classic activator, is able to activate transcription independently through two different mechanisms. Understanding the class I mechanism requires an intact transcription activation complex (TAC) structure at a high resolution. Here we report a high-resolution cryo-electron microscopy structure of an intact Escherichia coli class I TAC containing a CAP dimer, a σ(70)-RNA polymerase (RNAP) holoenzyme, a complete class I CAP-dependent promoter DNA, and a de novo synthesized RNA oligonucleotide. The structure shows how CAP wraps the upstream DNA and how the interactions recruit RNAP. Our study provides a structural basis for understanding how activators activate transcription through the class I recruitment mechanism.

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11/10/17 | larvalign: Aligning gene expression patterns from the larval brain of Drosophila melanogaster.
Muenzing SE, Strauch M, Truman JW, Bühler K, Thum AS, Merhof D
Neuroinformatics. 2017 Nov 10:. doi: 10.1007/s12021-017-9349-6

The larval brain of the fruit fly Drosophila melanogaster is a small, tractable model system for neuroscience. Genes for fluorescent marker proteins can be expressed in defined, spatially restricted neuron populations. Here, we introduce the methods for 1) generating a standard template of the larval central nervous system (CNS), 2) spatial mapping of expression patterns from different larvae into a reference space defined by the standard template. We provide a manually annotated gold standard that serves for evaluation of the registration framework involved in template generation and mapping. A method for registration quality assessment enables the automatic detection of registration errors, and a semi-automatic registration method allows one to correct registrations, which is a prerequisite for a high-quality, curated database of expression patterns. All computational methods are available within the larvalign software package: https://github.com/larvalign/larvalign/releases/tag/v1.0.

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