Filter
Associated Lab
- Ahrens Lab (7) Apply Ahrens Lab filter
- Aso Lab (2) Apply Aso Lab filter
- Baker Lab (2) Apply Baker Lab filter
- Betzig Lab (12) Apply Betzig Lab filter
- Bock Lab (1) Apply Bock Lab filter
- Branson Lab (5) Apply Branson Lab filter
- Card Lab (3) Apply Card Lab filter
- Cardona Lab (8) Apply Cardona Lab filter
- Dickson Lab (4) Apply Dickson Lab filter
- Druckmann Lab (3) Apply Druckmann Lab filter
- Dudman Lab (3) Apply Dudman Lab filter
- Eddy/Rivas Lab (1) Apply Eddy/Rivas Lab filter
- Egnor Lab (1) Apply Egnor Lab filter
- Fetter Lab (6) Apply Fetter Lab filter
- Fitzgerald Lab (1) Apply Fitzgerald Lab filter
- Freeman Lab (4) Apply Freeman Lab filter
- Funke Lab (2) Apply Funke Lab filter
- Gonen Lab (8) Apply Gonen Lab filter
- Grigorieff Lab (6) Apply Grigorieff Lab filter
- Harris Lab (5) Apply Harris Lab filter
- Hess Lab (2) Apply Hess Lab filter
- Jayaraman Lab (3) Apply Jayaraman Lab filter
- Ji Lab (4) Apply Ji Lab filter
- Kainmueller Lab (3) Apply Kainmueller Lab filter
- Karpova Lab (3) Apply Karpova Lab filter
- Keleman Lab (1) Apply Keleman Lab filter
- Keller Lab (6) Apply Keller Lab filter
- Lavis Lab (13) Apply Lavis Lab filter
- Lee (Albert) Lab (1) Apply Lee (Albert) Lab filter
- Leonardo Lab (2) Apply Leonardo Lab filter
- Li Lab (2) Apply Li Lab filter
- Lippincott-Schwartz Lab (9) Apply Lippincott-Schwartz Lab filter
- Liu (Zhe) Lab (4) Apply Liu (Zhe) Lab filter
- Looger Lab (10) Apply Looger Lab filter
- Magee Lab (2) Apply Magee Lab filter
- Menon Lab (2) Apply Menon Lab filter
- Murphy Lab (2) Apply Murphy Lab filter
- Pachitariu Lab (2) Apply Pachitariu Lab filter
- Pastalkova Lab (1) Apply Pastalkova Lab filter
- Pavlopoulos Lab (3) Apply Pavlopoulos Lab filter
- Pedram Lab (1) Apply Pedram Lab filter
- Podgorski Lab (1) Apply Podgorski Lab filter
- Reiser Lab (3) Apply Reiser Lab filter
- Romani Lab (2) Apply Romani Lab filter
- Rubin Lab (3) Apply Rubin Lab filter
- Saalfeld Lab (3) Apply Saalfeld Lab filter
- Schreiter Lab (2) Apply Schreiter Lab filter
- Simpson Lab (1) Apply Simpson Lab filter
- Singer Lab (5) Apply Singer Lab filter
- Spruston Lab (6) Apply Spruston Lab filter
- Stern Lab (7) Apply Stern Lab filter
- Sternson Lab (3) Apply Sternson Lab filter
- Stringer Lab (1) Apply Stringer Lab filter
- Svoboda Lab (8) Apply Svoboda Lab filter
- Tervo Lab (2) Apply Tervo Lab filter
- Tillberg Lab (1) Apply Tillberg Lab filter
- Tjian Lab (3) Apply Tjian Lab filter
- Truman Lab (11) Apply Truman Lab filter
- Turaga Lab (2) Apply Turaga Lab filter
- Turner Lab (1) Apply Turner Lab filter
- Wang (Shaohe) Lab (2) Apply Wang (Shaohe) Lab filter
- Wu Lab (1) Apply Wu Lab filter
- Zlatic Lab (2) Apply Zlatic Lab filter
Associated Project Team
- Fly Functional Connectome (2) Apply Fly Functional Connectome filter
- Fly Olympiad (1) Apply Fly Olympiad filter
- FlyEM (1) Apply FlyEM filter
- GENIE (2) Apply GENIE filter
- MouseLight (2) Apply MouseLight filter
- Tool Translation Team (T3) (1) Apply Tool Translation Team (T3) filter
- Transcription Imaging (11) Apply Transcription Imaging filter
Publication Date
- December 2016 (18) Apply December 2016 filter
- November 2016 (15) Apply November 2016 filter
- October 2016 (22) Apply October 2016 filter
- September 2016 (11) Apply September 2016 filter
- August 2016 (13) Apply August 2016 filter
- July 2016 (15) Apply July 2016 filter
- June 2016 (25) Apply June 2016 filter
- May 2016 (23) Apply May 2016 filter
- April 2016 (14) Apply April 2016 filter
- March 2016 (15) Apply March 2016 filter
- February 2016 (23) Apply February 2016 filter
- January 2016 (15) Apply January 2016 filter
- Remove 2016 filter 2016
Type of Publication
209 Publications
Showing 31-40 of 209 resultsNeuroscience research is becoming increasingly more collaborative and interdisciplinary with partnerships between industry and academia and insights from fields beyond neuroscience. In the age of institutional initiatives and multi-investigator collaborations, scientists from around the world shared their perspectives on the effectiveness of large-scale collaborations versus single-lab, hypothesis-driven science.
The revolution in neuroscientific data acquisition is creating an analysis challenge. We propose leveraging cloud-computing technologies to enable large-scale neurodata storing, exploring, analyzing, and modeling. This utility will empower scientists globally to generate and test theories of brain function and dysfunction.
We propose a framework for detecting action patterns from motion sequences and modeling the sensory-motor relationship of animals, using a generative recurrent neural network. The network has a discriminative part (classifying actions) and a generative part (predicting motion), whose recurrent cells are laterally connected, allowing higher levels of the network to represent high level phenomena. We test our framework on two types of data, fruit fly behavior and online handwriting. Our results show that 1) taking advantage of unlabeled sequences, by predicting future motion, significantly improves action detection performance when training labels are scarce, 2) the network learns to represent high level phenomena such as writer identity and fly gender, without supervision, and 3) simulated motion trajectories, generated by treating motion prediction as input to the network, look realistic and may be used to qualitatively evaluate whether the model has learnt generative control rules.
Optimal image quality in light-sheet microscopy requires a perfect overlap between the illuminating light sheet and the focal plane of the detection objective. However, mismatches between the light-sheet and detection planes are common owing to the spatiotemporally varying optical properties of living specimens. Here we present the AutoPilot framework, an automated method for spatiotemporally adaptive imaging that integrates (i) a multi-view light-sheet microscope capable of digitally translating and rotating light-sheet and detection planes in three dimensions and (ii) a computational method that continuously optimizes spatial resolution across the specimen volume in real time. We demonstrate long-term adaptive imaging of entire developing zebrafish (Danio rerio) and Drosophila melanogaster embryos and perform adaptive whole-brain functional imaging in larval zebrafish. Our method improves spatial resolution and signal strength two to five-fold, recovers cellular and sub-cellular structures in many regions that are not resolved by non-adaptive imaging, adapts to spatiotemporal dynamics of genetically encoded fluorescent markers and robustly optimizes imaging performance during large-scale morphogenetic changes in living organisms.
Actin assembly and inward flow in the plane of the immunological synapse (IS) drives the centralization of T cell receptor microclusters (TCR MCs) and the integrin leukocyte functional antigen 1 (LFA-1). Using structured-illumination microscopy (SIM), we show that actin arcs populating the medial, lamella-like region of the IS arise from linear actin filaments generated by one or more formins present at the IS distal edge. After traversing the outer, Arp2/3-generated, lamellipodia-like region of the IS, these linear filaments are organized by myosin II into antiparallel concentric arcs. Three-dimensional SIM shows that active LFA-1 often aligns with arcs, whereas TCR MCs commonly reside between arcs, and total internal reflection fluorescence SIM shows TCR MCs being swept inward by arcs. Consistently, disrupting actin arc formation via formin inhibition results in less centralized TCR MCs, missegregated integrin clusters, decreased T-B cell adhesion, and diminished TCR signaling. Together, our results define the origin, organization, and functional significance of a major actomyosin contractile structure at the IS that directly propels TCR MC transport.
Naïve Bayes Nearest Neighbour (NBNN) is a simple and effective framework which addresses many of the pitfalls of K-Nearest Neighbour (KNN) classification. It has yielded competitive results on several computer vision benchmarks. Its central tenet is that during NN search, a query is not compared to every example in a database, ignoring class information. Instead, NN searches are performed within each class, generating a score per class. A key problem with NN techniques, including NBNN, is that they fail when the data representation does not capture perceptual (e.g. class-based) similarity. NBNN circumvents this by using independent engineered descriptors (e.g. SIFT). To extend its applicability outside of image-based domains, we propose to learn a metric which captures perceptual similarity. Similar to how Neighbourhood Components Analysis optimizes a differentiable form of KNN classification, we propose 'Class Conditional' metric learning (CCML), which optimizes a soft form of the NBNN selection rule. Typical metric learning algorithms learn either a global or local metric. However, our proposed method can be adjusted to a particular level of locality by tuning a single parameter. An empirical evaluation on classification and retrieval tasks demonstrates that our proposed method clearly outperforms existing learned distance metrics across a variety of image and non-image datasets.
Mitochondrial damage is the major factor underlying drug-induced liver disease but whether conditions that thwart mitochondrial injury can prevent or reverse drug-induced liver damage is unclear. A key molecule regulating mitochondria quality control is AMP activated kinase (AMPK). When activated, AMPK causes mitochondria to elongate/fuse and proliferate, with mitochondria now producing more ATP and less reactive oxygen species. Autophagy is also triggered, a process capable of removing damaged/defective mitochondria. To explore whether AMPK activation could potentially prevent or reverse the effects of drug-induced mitochondrial and hepatocellular damage, we added an AMPK activator to collagen sandwich cultures of rat and human hepatocytes exposed to the hepatotoxic drugs, acetaminophen or diclofenac. In the absence of AMPK activation, the drugs caused hepatocytes to lose polarized morphology and have significantly decreased ATP levels and viability. At the subcellular level, mitochondria underwent fragmentation and had decreased membrane potential due to decreased expression of the mitochondrial fusion proteins Mfn1, 2 and/or Opa1. Adding AICAR, a specific AMPK activator, at the time of drug exposure prevented and reversed these effects. The mitochondria became highly fused and ATP production increased, and hepatocytes maintained polarized morphology. In exploring the mechanism responsible for this preventive and reversal effect, we found that AMPK activation prevented drug-mediated decreases in Mfn1, 2 and Opa1. AMPK activation also stimulated autophagy/mitophagy, most significantly in acetaminophen-treated cells. These results suggest that activation of AMPK prevents/reverses drug-induced mitochondrial and hepatocellular damage through regulation of mitochondrial fusion and autophagy, making it a potentially valuable approach for treatment of drug-induced liver injury.
The endoplasmic reticulum (ER) is an expansive, membrane-enclosed organelle that plays crucial roles in numerous cellular functions. We used emerging superresolution imaging technologies to clarify the morphology and dynamics of the peripheral ER, which contacts and modulates most other intracellular organelles. Peripheral components of the ER have classically been described as comprising both tubules and flat sheets. We show that this system consists almost exclusively of tubules at varying densities, including structures that we term ER matrices. Conventional optical imaging technologies had led to misidentification of these structures as sheets because of the dense clustering of tubular junctions and a previously uncharacterized rapid form of ER motion. The existence of ER matrices explains previous confounding evidence that had indicated the occurrence of ER “sheet” proliferation after overexpression of tubular junction–forming proteins.
Johnston’s organ is the largest mechanosensory organ in Drosophila; it analyzes movements of the antenna due to sound, wind, gravity, and touch. Different Johnston’s organ neurons (JONs) encode distinct stimulus features. Certain JONs respond in a sustained manner to steady displacements, and these JONs subdivide into opponent populations that prefer push or pull displacements. Here, we describe neurons in the brain (aPN3 neurons) that combine excitation and inhibition from push/pull JONs in different ratios. Consequently, different aPN3 neurons are sensitive to movement in different parts of the antenna’s range, at different frequencies, or at different amplitude modulation rates. We use a model to show how the tuning of aPN3 neurons can arise from rectification and temporal filtering in JONs, followed by mixing of JON signals in different proportions. These results illustrate how several canonical neural circuit components—rectification, opponency, and filtering—can combine to produce selectivity for complex stimulus features.
To execute accurate movements, animals must continuously adapt their behavior to changes in their bodies and environments. Animals can learn changes in the relationship between their locomotor commands and the resulting distance moved, then adjust command strength to achieve a desired travel distance. It is largely unknown which circuits implement this form of motor learning, or how. Using whole-brain neuronal imaging and circuit manipulations in larval zebrafish, we discovered that the serotonergic dorsal raphe nucleus (DRN) mediates short-term locomotor learning. Serotonergic DRN neurons respond phasically to swim-induced visual motion, but little to motion that is not self-generated. During prolonged exposure to a given motosensory gain, persistent DRN activity emerges that stores the learned efficacy of motor commands and adapts future locomotor drive for tens of seconds. The DRN’s ability to track the effectiveness of motor intent may constitute a computational building block for the broader functions of the serotonergic system.