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1287 Janelia Publications

Showing 11-20 of 1287 results
Reiser LabRubin LabFly Functional Connectome
12/18/17 | Behavioral state modulates the ON visual motion pathway of Drosophila.
Strother JA, Wu S, Rogers EM, Eliason JL, Wong AM, Nern A, Reiser MB
Proceedings of the National Academy of Sciences of the United States of America. 2017 Dec 18;115(1):E102-11. doi: 10.1073/pnas.1703090115

The behavioral state of an animal can dynamically modulate visual processing. In flies, the behavioral state is known to alter the temporal tuning of neurons that carry visual motion information into the central brain. However, where this modulation occurs and how it tunes the properties of this neural circuit are not well understood. Here, we show that the behavioral state alters the baseline activity levels and the temporal tuning of the first directionally selective neuron in the ON motion pathway (T4) as well as its primary input neurons (Mi1, Tm3, Mi4, Mi9). These effects are especially prominent in the inhibitory neuron Mi4, and we show that central octopaminergic neurons provide input to Mi4 and increase its excitability. We further show that octopamine neurons are required for sustained behavioral responses to fast-moving, but not slow-moving, visual stimuli in walking flies. These results indicate that behavioral-state modulation acts directly on the inputs to the directionally selective neurons and supports efficient neural coding of motion stimuli.

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12/15/17 | Stability, affinity and chromatic variants of the glutamate sensor iGluSnFR.
Marvin JS, Scholl B, Wilson DE, Podgorski K, Kazemipour A, Mueller JA, Schoch-McGovern S, Wang SS, Quiroz FJ, Rebola N, Bao H, Little JP, Tkachuk AN, Hantman AW, Chapman ER, Dietrich D, DiGregorio DA, Fitzpatrick D, Looger LL
bioRxiv. 2017 Dec 15:235176. doi: 10.1101/235176

Single-wavelength fluorescent reporters allow visualization of specific neurotransmitters with high spatial and temporal resolution. We report variants of the glutamate sensor iGluSnFR that are functionally brighter; can detect sub-micromolar to millimolar concentrations of glutamate; and have blue, green or yellow emission profiles. These variants allow in vivo imaging where original-iGluSnFR was too dim, reveal glutamate transients at individual spine heads, and permit kilohertz imaging with inexpensive, powerful fiber lasers.

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12/12/17 | BIM for Facilities Management: Providing value at the Howard Hughes Medical Institute.
Wang G, Philip M, McKinley M
Journal of the National Institute of Building Sciences. 2017 Winter;5(3):10-14

While building information modeling (BIM) is widely embraced by the architectural, engineering and construction (AEC) industry, BIM adoption in facilities management (FM) is still relatively new and limited. BIM deliverables from design and construction generally do not fulfill FM needs unless they are clearly specified and carefully managed.

The Facilities Group responsible for the Janelia Research Campus of the Howard Hughes Medical Institute (HHMI) expects any BIM platform to provide value in operations and maintenance. Janelia’s BIM vision goes beyond transferring BIM data to computerized maintenance management software (CMMS) and integrated workplace management system (IWMS) platforms. Instead, Janelia creates and maintains FM-capable BIM, utilizes the models to solve operational challenges and improves safety and efficiency in various ways, including engineering analysis for heating, ventilation and air conditioning (HVAC), electrical and plumbing; building automation systems (BAS) analysis; operational impact analysis; and BIM-aided operation safety.

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12/12/17 | Chemogenetic tools for causal cellular and neuronal biology.
Atasoy D, Sternson SM
Physiological Reviews. 2017 Dec 12:. doi: 10.1152/physrev.00009.2017

Chemogenetic technologies enable selective pharmacological control of specific cell populations. An increasing number of approaches have been developed that modulate different signaling pathways. Selective pharmacological control over G protein-coupled receptor signaling, ion channel conductances, protein association, protein stability, and small molecule targeting allows modulation of cellular processes in distinct cell types. Here, we review these chemogenetic technologies and instances of their applications in complex tissues in vivo and ex vivo.

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12/11/17 | Clustering single cells: a review of approaches on high-and low-depth single-cell RNA-seq data.
Menon V
Briefings in Functional Genomics. 2017 Dec 11:. doi: 10.1093/bfgp/elx044

Advances in single-cell RNA-sequencing technology have resulted in a wealth of studies aiming to identify transcriptomic cell types in various biological systems. There are multiple experimental approaches to isolate and profile single cells, which provide different levels of cellular and tissue coverage. In addition, multiple computational strategies have been proposed to identify putative cell types from single-cell data. From a data generation perspective, recent single-cell studies can be classified into two groups: those that distribute reads shallowly over large numbers of cells and those that distribute reads more deeply over a smaller cell population. Although there are advantages to both approaches in terms of cellular and tissue coverage, it is unclear whether different computational cell type identification methods are better suited to one or the other experimental paradigm. This study reviews three cell type clustering algorithms, each representing one of three broad approaches, and finds that PCA-based algorithms appear most suited to low read depth data sets, whereas gene clustering-based and biclustering algorithms perform better on high read depth data sets. In addition, highly related cell classes are better distinguished by higher-depth data, given the same total number of reads; however, simultaneous discovery of distinct and similar types is better served by lower-depth, higher cell number data. Overall, this study suggests that the depth of profiling should be determined by initial assumptions about the diversity of cells in the population, and that the selection of clustering algorithm(s) is subsequently based on the depth of profiling will allow for better identification of putative transcriptomic cell types.

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12/11/17 | The functional organization of descending sensory-motor pathways in Drosophila.
Namiki S, Dickinson MH, Wong AM, Korff W, Card GM
bioRxiv. 2017 Dec 11:231696. doi: 10.1101/231696

In most animals, the brain controls the body via a set of descending neurons (DNs) that traverse the neck and terminate in post-cranial regions of the nervous system. This critical neural population is thought to activate, maintain and modulate locomotion and other behaviors. Although individual members of this cell class have been well-studied across species ranging from insects to primates, little is known about the overall connectivity pattern of DNs as a population. We undertook a systematic anatomical investigation of descending neurons in the fruit fly, Drosophila melanogaster, and created a collection of over 100 transgenic lines targeting individual cell types. Our methods allowed us to describe the morphology of roughly half of an estimated 400 DNs and create a comprehensive map of connectivity between the sensory neuropils in the brain and the motor neuropils in the ventral nerve cord. Like the vertebrate spinal cord, our results show that the fly nerve cord is a highly organized, layered system of neuropils, an organization that reflects the fact that insects are capable of two largely independent means of locomotion -- walking and fight -- using distinct sets of appendages. Our results reveal the basic functional map of descending pathways in flies and provide tools for systematic interrogation of sensory-motor circuits.

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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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