Filter
Associated Lab
- Aguilera Castrejon Lab (1) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (2) Apply Ahrens Lab filter
- Aso Lab (3) Apply Aso Lab filter
- Baker Lab (1) Apply Baker Lab filter
- Betzig Lab (8) Apply Betzig Lab filter
- Beyene Lab (2) Apply Beyene Lab filter
- Bock Lab (1) Apply Bock Lab filter
- Branson Lab (7) Apply Branson Lab filter
- Card Lab (4) Apply Card Lab filter
- Cardona Lab (8) Apply Cardona Lab filter
- Cui Lab (1) Apply Cui Lab filter
- Darshan Lab (1) Apply Darshan Lab filter
- Dickson Lab (1) Apply Dickson Lab filter
- Druckmann Lab (3) Apply Druckmann Lab filter
- Dudman Lab (4) Apply Dudman Lab filter
- Eddy/Rivas Lab (1) Apply Eddy/Rivas Lab filter
- Feliciano Lab (1) Apply Feliciano Lab filter
- Fetter Lab (4) Apply Fetter Lab filter
- Funke Lab (1) Apply Funke Lab filter
- Gonen Lab (11) Apply Gonen Lab filter
- Grigorieff Lab (6) Apply Grigorieff Lab filter
- Harris Lab (5) Apply Harris Lab filter
- Heberlein Lab (1) Apply Heberlein Lab filter
- Hermundstad Lab (1) Apply Hermundstad Lab filter
- Hess Lab (4) Apply Hess Lab filter
- Jayaraman Lab (4) Apply Jayaraman Lab filter
- Ji Lab (5) Apply Ji Lab filter
- Johnson Lab (1) Apply Johnson Lab filter
- Kainmueller Lab (1) Apply Kainmueller Lab filter
- Keleman Lab (1) Apply Keleman Lab filter
- Keller Lab (2) Apply Keller Lab filter
- Koay Lab (3) Apply Koay Lab filter
- Lavis Lab (16) Apply Lavis Lab filter
- Lee (Albert) Lab (6) Apply Lee (Albert) Lab filter
- Leonardo Lab (2) Apply Leonardo Lab filter
- Li Lab (4) Apply Li Lab filter
- Lippincott-Schwartz Lab (11) Apply Lippincott-Schwartz Lab filter
- Liu (Zhe) Lab (5) Apply Liu (Zhe) Lab filter
- Looger Lab (6) Apply Looger Lab filter
- Magee Lab (2) Apply Magee Lab filter
- Menon Lab (1) Apply Menon Lab filter
- Pachitariu Lab (5) Apply Pachitariu Lab filter
- Pedram Lab (1) Apply Pedram Lab filter
- Reiser Lab (6) Apply Reiser Lab filter
- Riddiford Lab (1) Apply Riddiford Lab filter
- Romani Lab (6) Apply Romani Lab filter
- Rubin Lab (15) Apply Rubin Lab filter
- Saalfeld Lab (4) Apply Saalfeld Lab filter
- Scheffer Lab (4) Apply Scheffer Lab filter
- Schreiter Lab (4) Apply Schreiter Lab filter
- Shroff Lab (1) Apply Shroff Lab filter
- Simpson Lab (2) Apply Simpson Lab filter
- Singer Lab (6) Apply Singer Lab filter
- Spruston Lab (1) Apply Spruston Lab filter
- Stern Lab (8) Apply Stern Lab filter
- Sternson Lab (2) Apply Sternson Lab filter
- Svoboda Lab (9) Apply Svoboda Lab filter
- Tebo Lab (3) Apply Tebo Lab filter
- Truman Lab (6) Apply Truman Lab filter
- Turaga Lab (3) Apply Turaga Lab filter
- Turner Lab (2) Apply Turner Lab filter
- Wang (Shaohe) Lab (4) Apply Wang (Shaohe) Lab filter
- Wu Lab (1) Apply Wu Lab filter
- Zlatic Lab (7) Apply Zlatic Lab filter
Associated Project Team
- Fly Descending Interneuron (1) Apply Fly Descending Interneuron filter
- Fly Functional Connectome (4) Apply Fly Functional Connectome filter
- Fly Olympiad (1) Apply Fly Olympiad filter
- FlyEM (4) Apply FlyEM filter
- FlyLight (2) Apply FlyLight filter
- GENIE (3) Apply GENIE filter
- ThalamoSeq (1) Apply ThalamoSeq filter
- Tool Translation Team (T3) (3) Apply Tool Translation Team (T3) filter
- Transcription Imaging (6) Apply Transcription Imaging filter
Publication Date
- December 2017 (16) Apply December 2017 filter
- November 2017 (13) Apply November 2017 filter
- October 2017 (10) Apply October 2017 filter
- September 2017 (14) Apply September 2017 filter
- August 2017 (18) Apply August 2017 filter
- July 2017 (25) Apply July 2017 filter
- June 2017 (19) Apply June 2017 filter
- May 2017 (27) Apply May 2017 filter
- April 2017 (22) Apply April 2017 filter
- March 2017 (19) Apply March 2017 filter
- February 2017 (10) Apply February 2017 filter
- January 2017 (21) Apply January 2017 filter
- Remove 2017 filter 2017
Type of Publication
214 Publications
Showing 11-20 of 214 resultsA 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.
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.
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.
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.
Quantitative analysis of the dynamic cellular mechanisms shaping the Drosophila wing during its larval growth phase has been limited, impeding our ability to understand how morphogen patterns regulate tissue shape. Such analysis requires explants to be imaged under conditions that maintain both growth and patterning, as well as methods to quantify how much cellular behaviors change tissue shape. Here, we demonstrate a key requirement for the steroid hormone 20-hydroxyecdysone (20E) in the maintenance of numerous patterning systems in vivo and in explant culture. We find that low concentrations of 20E support prolonged proliferation in explanted wing discs in the absence of insulin, incidentally providing novel insight into the hormonal regulation of imaginal growth. We use 20E-containing media to observe growth directly and to apply recently developed methods for quantitatively decomposing tissue shape changes into cellular contributions. We discover that whereas cell divisions drive tissue expansion along one axis, their contribution to expansion along the orthogonal axis is cancelled by cell rearrangements and cell shape changes. This finding raises the possibility that anisotropic mechanical constraints contribute to growth orientation in the wing disc.
Cell invasion across basement membrane barriers is important in both normal development and cancer metastasis. In this issue of Developmental Cell, Naegeli et al. (2017) identify a mechanism for breaching basement membranes. Localized lysosome exocytosis fuels generation of large, invasive cellular protrusions that expand tiny basement membrane openings.
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.
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.
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.