Filter
Associated Lab
- Aguilera Castrejon Lab (17) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (66) Apply Ahrens Lab filter
- Aso Lab (40) Apply Aso Lab filter
- Baker Lab (38) Apply Baker Lab filter
- Betzig Lab (113) Apply Betzig Lab filter
- Beyene Lab (14) Apply Beyene Lab filter
- Bock Lab (17) Apply Bock Lab filter
- Branson Lab (54) Apply Branson Lab filter
- Card Lab (43) Apply Card Lab filter
- Cardona Lab (64) Apply Cardona Lab filter
- Chklovskii Lab (13) Apply Chklovskii Lab filter
- Clapham Lab (15) Apply Clapham Lab filter
- Cui Lab (19) Apply Cui Lab filter
- Darshan Lab (12) Apply Darshan Lab filter
- Dennis Lab (1) Apply Dennis Lab filter
- Dickson Lab (46) Apply Dickson Lab filter
- Druckmann Lab (25) Apply Druckmann Lab filter
- Dudman Lab (50) Apply Dudman Lab filter
- Eddy/Rivas Lab (30) Apply Eddy/Rivas Lab filter
- Egnor Lab (11) Apply Egnor Lab filter
- Espinosa Medina Lab (19) Apply Espinosa Medina Lab filter
- Feliciano Lab (8) Apply Feliciano Lab filter
- Fetter Lab (41) Apply Fetter Lab filter
- FIB-SEM Technology (1) Apply FIB-SEM Technology filter
- Fitzgerald Lab (29) Apply Fitzgerald Lab filter
- Freeman Lab (15) Apply Freeman Lab filter
- Funke Lab (39) Apply Funke Lab filter
- Gonen Lab (91) Apply Gonen Lab filter
- Grigorieff Lab (62) Apply Grigorieff Lab filter
- Harris Lab (63) Apply Harris Lab filter
- Heberlein Lab (94) Apply Heberlein Lab filter
- Hermundstad Lab (28) Apply Hermundstad Lab filter
- Hess Lab (77) Apply Hess Lab filter
- Ilanges Lab (2) Apply Ilanges Lab filter
- Jayaraman Lab (46) Apply Jayaraman Lab filter
- Ji Lab (33) Apply Ji Lab filter
- Johnson Lab (6) Apply Johnson Lab filter
- Kainmueller Lab (19) Apply Kainmueller Lab filter
- Karpova Lab (14) Apply Karpova Lab filter
- Keleman Lab (13) Apply Keleman Lab filter
- Keller Lab (76) Apply Keller Lab filter
- Koay Lab (18) Apply Koay Lab filter
- Lavis Lab (151) Apply Lavis Lab filter
- Lee (Albert) Lab (34) Apply Lee (Albert) Lab filter
- Leonardo Lab (23) Apply Leonardo Lab filter
- Li Lab (28) Apply Li Lab filter
- Lippincott-Schwartz Lab (172) Apply Lippincott-Schwartz Lab filter
- Liu (Yin) Lab (7) Apply Liu (Yin) Lab filter
- Liu (Zhe) Lab (64) Apply Liu (Zhe) Lab filter
- Looger Lab (138) Apply Looger Lab filter
- Magee Lab (49) Apply Magee Lab filter
- Menon Lab (18) Apply Menon Lab filter
- Murphy Lab (13) Apply Murphy Lab filter
- O'Shea Lab (7) Apply O'Shea Lab filter
- Otopalik Lab (13) Apply Otopalik Lab filter
- Pachitariu Lab (48) Apply Pachitariu Lab filter
- Pastalkova Lab (18) Apply Pastalkova Lab filter
- Pavlopoulos Lab (19) Apply Pavlopoulos Lab filter
- Pedram Lab (15) Apply Pedram Lab filter
- Podgorski Lab (16) Apply Podgorski Lab filter
- Reiser Lab (51) Apply Reiser Lab filter
- Riddiford Lab (44) Apply Riddiford Lab filter
- Romani Lab (43) Apply Romani Lab filter
- Rubin Lab (145) Apply Rubin Lab filter
- Saalfeld Lab (63) Apply Saalfeld Lab filter
- Satou Lab (16) Apply Satou Lab filter
- Scheffer Lab (36) Apply Scheffer Lab filter
- Schreiter Lab (68) Apply Schreiter Lab filter
- Sgro Lab (21) Apply Sgro Lab filter
- Shroff Lab (31) Apply Shroff Lab filter
- Simpson Lab (23) Apply Simpson Lab filter
- Singer Lab (80) Apply Singer Lab filter
- Spruston Lab (94) Apply Spruston Lab filter
- Stern Lab (156) Apply Stern Lab filter
- Sternson Lab (54) Apply Sternson Lab filter
- Stringer Lab (36) Apply Stringer Lab filter
- Svoboda Lab (135) Apply Svoboda Lab filter
- Tebo Lab (33) Apply Tebo Lab filter
- Tervo Lab (9) Apply Tervo Lab filter
- Tillberg Lab (21) Apply Tillberg Lab filter
- Tjian Lab (64) Apply Tjian Lab filter
- Truman Lab (88) Apply Truman Lab filter
- Turaga Lab (52) Apply Turaga Lab filter
- Turner Lab (39) Apply Turner Lab filter
- Vale Lab (8) Apply Vale Lab filter
- Voigts Lab (3) Apply Voigts Lab filter
- Wang (Meng) Lab (22) Apply Wang (Meng) Lab filter
- Wang (Shaohe) Lab (25) Apply Wang (Shaohe) Lab filter
- Wu Lab (9) Apply Wu Lab filter
- Zlatic Lab (28) Apply Zlatic Lab filter
- Zuker Lab (25) Apply Zuker Lab filter
Associated Project Team
- CellMap (12) Apply CellMap filter
- COSEM (3) Apply COSEM filter
- FIB-SEM Technology (3) Apply FIB-SEM Technology filter
- Fly Descending Interneuron (11) Apply Fly Descending Interneuron filter
- Fly Functional Connectome (14) Apply Fly Functional Connectome filter
- Fly Olympiad (5) Apply Fly Olympiad filter
- FlyEM (54) Apply FlyEM filter
- FlyLight (49) Apply FlyLight filter
- GENIE (47) Apply GENIE filter
- Integrative Imaging (6) Apply Integrative Imaging filter
- Larval Olympiad (2) Apply Larval Olympiad filter
- MouseLight (18) Apply MouseLight filter
- NeuroSeq (1) Apply NeuroSeq filter
- ThalamoSeq (1) Apply ThalamoSeq filter
- Tool Translation Team (T3) (27) Apply Tool Translation Team (T3) filter
- Transcription Imaging (49) Apply Transcription Imaging filter
Publication Date
- 2025 (160) Apply 2025 filter
- 2024 (214) Apply 2024 filter
- 2023 (159) Apply 2023 filter
- 2022 (192) Apply 2022 filter
- 2021 (194) Apply 2021 filter
- 2020 (196) Apply 2020 filter
- 2019 (202) Apply 2019 filter
- 2018 (232) Apply 2018 filter
- 2017 (217) Apply 2017 filter
- 2016 (209) Apply 2016 filter
- 2015 (252) Apply 2015 filter
- 2014 (236) Apply 2014 filter
- 2013 (194) Apply 2013 filter
- 2012 (190) Apply 2012 filter
- 2011 (190) Apply 2011 filter
- 2010 (161) Apply 2010 filter
- 2009 (158) Apply 2009 filter
- 2008 (140) Apply 2008 filter
- 2007 (106) Apply 2007 filter
- 2006 (92) Apply 2006 filter
- 2005 (67) Apply 2005 filter
- 2004 (57) Apply 2004 filter
- 2003 (58) Apply 2003 filter
- 2002 (39) Apply 2002 filter
- 2001 (28) Apply 2001 filter
- 2000 (29) Apply 2000 filter
- 1999 (14) Apply 1999 filter
- 1998 (18) Apply 1998 filter
- 1997 (16) Apply 1997 filter
- 1996 (10) Apply 1996 filter
- 1995 (18) Apply 1995 filter
- 1994 (12) Apply 1994 filter
- 1993 (10) Apply 1993 filter
- 1992 (6) Apply 1992 filter
- 1991 (11) Apply 1991 filter
- 1990 (11) Apply 1990 filter
- 1989 (6) Apply 1989 filter
- 1988 (1) Apply 1988 filter
- 1987 (7) Apply 1987 filter
- 1986 (4) Apply 1986 filter
- 1985 (5) Apply 1985 filter
- 1984 (2) Apply 1984 filter
- 1983 (2) Apply 1983 filter
- 1982 (3) Apply 1982 filter
- 1981 (3) Apply 1981 filter
- 1980 (1) Apply 1980 filter
- 1979 (1) Apply 1979 filter
- 1976 (2) Apply 1976 filter
- 1973 (1) Apply 1973 filter
- 1970 (1) Apply 1970 filter
- 1967 (1) Apply 1967 filter
Type of Publication
4138 Publications
Showing 1351-1360 of 4138 resultsIntercepting a moving object requires prediction of its future location. This complex task has been solved by dragonflies, who intercept their prey in midair with a 95% success rate. In this study, we show that a group of 16 neurons, called target-selective descending neurons (TSDNs), code a population vector that reflects the direction of the target with high accuracy and reliability across 360°. The TSDN spatial (receptive field) and temporal (latency) properties matched the area of the retina where the prey is focused and the reaction time, respectively, during predatory flights. The directional tuning curves and morphological traits (3D tracings) for each TSDN type were consistent among animals, but spike rates were not. Our results emphasize that a successful neural circuit for target tracking and interception can be achieved with few neurons and that in dragonflies this information is relayed from the brain to the wing motor centers in population vector form.
The reward system is a collection of circuits that reinforce behaviors necessary for survival [1, 2]. Given the importance of reproduction for survival, actions that promote successful mating induce pleasurable feeling and are positively reinforced [3, 4]. This principle is conserved in Drosophila, where successful copulation is naturally rewarding to male flies, induces long-term appetitive memories [5], increases brain levels of neuropeptide F (NPF, the fly homolog of neuropeptide Y), and prevents ethanol, known otherwise as rewarding to flies [6, 7], from being rewarding [5]. It is not clear which of the multiple sensory and motor responses performed during mating induces perception of reward. Sexual interactions with female flies that do not reach copulation are not sufficient to reduce ethanol consumption [5], suggesting that only successful mating encounters are rewarding. Here, we uncoupled the initial steps of mating from its final steps and tested the ability of ejaculation to mimic the rewarding value of full copulation. We induced ejaculation by activating neurons that express the neuropeptide corazonin (CRZ) [8] and subsequently measured different aspects of reward. We show that activating Crz-expressing neurons is rewarding to male flies, as they choose to reside in a zone that triggers optogenetic stimulation of Crz neurons and display conditioned preference for an odor paired with the activation. Reminiscent of successful mating, repeated activation of Crz neurons increases npf levels and reduces ethanol consumption. Our results demonstrate that ejaculation stimulated by Crz/Crz-receptor signaling serves as an essential part of the mating reward mechanism in Drosophila. VIDEO ABSTRACT.
Anatomy of large biological specimens is often reconstructed from serially sectioned volumes imaged by high-resolution microscopy. We developed a method to reassemble a continuous volume from such large section series that explicitly minimizes artificial deformation by applying a global elastic constraint. We demonstrate our method on a series of transmission electron microscopy sections covering the entire 558-cell Caenorhabditis elegans embryo and a segment of the Drosophila melanogaster larval ventral nerve cord.
Parallel circuits throughout the CNS exhibit distinct sensitivities and responses to sensory stimuli. Ambiguities in the source and properties of signals elicited by physiological stimuli, however, frequently obscure the mechanisms underlying these distinctions. We found that differences in the degree to which activity in two classes of Off retinal ganglion cell (RGC) encode information about light stimuli near detection threshold were not due to obvious differences in the cells’ intrinsic properties or the chemical synaptic input the cells received; indeed, differences in the cells’ light responses were largely insensitive to block of fast ionotropic glutamate receptors. Instead, the distinct responses of the two types of RGCs likely reflect differences in light-evoked electrical synaptic input. These results highlight a surprising strategy by which the retina differentially processes and routes visual information and provide new insight into the circuits that underlie responses to stimuli near detection threshold.
Artificial lipidic bilayers are widely used as a model for the lipid matrix in biological cell membranes. We use the Pockels electro-optical effect to investigate the properties of an artificial lipidic membrane doped with nonlinear molecules in the outer layer. We report here what is believed to be the first electro-optical Pockels signal and image from such a membrane. The electro-optical dephasing distribution within the membrane is imaged and the signal is shown to be linear as a function of the applied voltage. A theoretical analysis taking into account the statistical orientation distribution of the inserted dye molecules allows us to estimate the doped membrane nonlinearity. Ongoing extensions of this work to living cell membranes are discussed.
The electro-optical Pockels response from a single non-centrosymmetric nanocrystal is reported. High sensitivity to the weak electric-field dependent nonlinear scattering is achieved through a dedicated imaging interferometric microscope and the linear dependence of electro-optical signal upon the applied field is checked. Using different incident light polarization states, a priori random spatial orientation of the crystal can be inferred. The electro-optical response from a nanocrystal provides local subwavelength sensor of quasi-static electric fields with potential applications in physics and biology. It also leads to a new sub-wavelength microscopy towards the nanoscale investigation of interesting phenomena such as nanoferroelectricity.
State-of-the-art silicon probes for electrical recording from neurons have thousands of recording sites. However, due to volume limitations there are typically many fewer wires carrying signals off the probe, which restricts the number of channels that can be recorded simultaneously. To overcome this fundamental constraint, we propose a method called electrode pooling that uses a single wire to serve many recording sites through a set of controllable switches. Here we present the framework behind this method and an experimental strategy to support it. We then demonstrate its feasibility by implementing electrode pooling on the Neuropixels 1.0 electrode array and characterizing its effect on signal and noise. Finally we use simulations to explore the conditions under which electrode pooling saves wires without compromising the content of the recordings. We make recommendations on the design of future devices to take advantage of this strategy.
The endosomal-sorting complex required for transport (ESCRT) is evolutionarily conserved from Archaea to eukaryotes. The complex drives membrane scission events in a range of processes, including cytokinesis in Metazoa and some Archaea. CdvA is the protein in Archaea that recruits ESCRT-III to the membrane. Using electron cryotomography (ECT), we find that CdvA polymerizes into helical filaments wrapped around liposomes. ESCRT-III proteins are responsible for the cinching of membranes and have been shown to assemble into helical tubes in vitro, but here we show that they also can form nested tubes and nested cones, which reveal surprisingly numerous and versatile contacts. To observe the ESCRT-CdvA complex in a physiological context, we used ECT to image the archaeon Sulfolobus acidocaldarius and observed a distinct protein belt at the leading edge of constriction furrows in dividing cells. The known dimensions of ESCRT-III proteins constrain their possible orientations within each of these structures and point to the involvement of spiraling filaments in membrane scission.
Aquaporins are a family of ubiquitous membrane proteins that form a pore for the permeation of water. Both electron and X-ray crystallography played major roles in determining the atomic structures of a number of aquaporins. This review focuses on electron crystallography, and its contribution to the field of aquaporin biology. We briefly discuss electron crystallography and the two-dimensional crystallization process. We describe features of aquaporins common to both electron and X-ray crystallographic structures; as well as some structural insights unique to electron crystallography, including aquaporin junction formation and lipid-protein interactions.
A central problem in neuroscience is reconstructing neuronal circuits on the synapse level. Due to a wide range of scales in brain architecture such reconstruction requires imaging that is both high-resolution and high-throughput. Existing electron microscopy (EM) techniques possess required resolution in the lateral plane and either high-throughput or high depth resolution but not both. Here, we exploit recent advances in unsupervised learning and signal processing to obtain high depth-resolution EM images computationally without sacrificing throughput. First, we show that the brain tissue can be represented as a sparse linear combination of localized basis functions that are learned using high-resolution datasets. We then develop compressive sensing-inspired techniques that can reconstruct the brain tissue from very few (typically 5) tomographic views of each section. This enables tracing of neuronal processes and, hence, high throughput reconstruction of neural circuits on the level of individual synapses.