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 281-290 of 4138 resultsSpontaneously blinking fluorophores permit the detection and localization of individual molecules without reducing buffers or caging groups, thus simplifying single-molecule localization microscopy (SMLM). The intrinsic blinking properties of such dyes are dictated by molecular structure and modulated by environment, which can limit utility. We report a series of tuned spontaneously blinking dyes with duty cycles that span two orders of magnitude, allowing facile SMLM in cells and dense biomolecular structures.
Chemical synapses between axons and dendrites mediate much of the brain’s intercellular communication. Here we describe a new kind of synapse – the axo-ciliary synapse - between axons and primary cilia. By employing enhanced focused ion beam – scanning electron microscopy on samples with optimally preserved ultrastructure, we discovered synapses between the serotonergic axons arising from the brainstem, and the primary cilia of hippocampal CA1 pyramidal neurons. Functionally, these cilia are enriched in a ciliary-restricted serotonin receptor, 5-hydroxytryptamine receptor 6 (HTR6), whose mutation is associated with learning and memory defects. Using a newly developed cilia-targeted serotonin sensor, we show that optogenetic stimulation of serotonergic axons results in serotonin release onto cilia. Ciliary HTR6 stimulation activates a non-canonical Gαq/11-RhoA pathway. Ablation of this pathway results in nuclear actin and chromatin accessibility changes in CA1 pyramidal neurons. Axo-ciliary synapses serve as a distinct mechanism for neuromodulators to program neuron transcription through privileged access to the nuclear compartment.
An important role of visual systems is to detect nearby predators, prey, and potential mates [1], which may be distinguished in part by their motion. When an animal is at rest, an object moving in any direction may easily be detected by motion-sensitive visual circuits [2, 3]. During locomotion, however, this strategy is compromised because the observer must detect a moving object within the pattern of optic flow created by its own motion through the stationary background. However, objects that move creating back-to-front (regressive) motion may be unambiguously distinguished from stationary objects because forward locomotion creates only front-to-back (progressive) optic flow. Thus, moving animals should exhibit an enhanced sensitivity to regressively moving objects. We explicitly tested this hypothesis by constructing a simple fly-sized robot that was programmed to interact with a real fly. Our measurements indicate that whereas walking female flies freeze in response to a regressively moving object, they ignore a progressively moving one. Regressive motion salience also explains observations of behaviors exhibited by pairs of walking flies. Because the assumptions underlying the regressive motion salience hypothesis are general, we suspect that the behavior we have observed in Drosophila may be widespread among eyed, motile organisms.
Artificial neural networks (ANNs) have been shown to predict neural responses in primary visual cortex (V1) better than classical models. However, this performance often comes at the expense of simplicity and interpretability. Here we introduce a new class of simplified ANN models that can predict over 70% of the response variance of V1 neurons. To achieve this high performance, we first recorded a new dataset of over 29,000 neurons responding to up to 65,000 natural image presentations in mouse V1. We found that ANN models required only two convolutional layers for good performance, with a relatively small first layer. We further found that we could make the second layer small without loss of performance, by fitting individual "minimodels" to each neuron. Similar simplifications applied for models of monkey V1 neurons. We show that the minimodels can be used to gain insight into how stimulus invariance arises in biological neurons. Preprint: https://www.biorxiv.org/content/early/2024/07/02/2024.06.30.601394
Molecular profiles of neurons influence neural development and function but bridging the gap between genes, circuits, and behavior has been very difficult. Here we used single cell RNAseq to generate a complete gene expression atlas of the Drosophila larval central nervous system composed of 131,077 single cells across three developmental stages (1 h, 24 h and 48 h after hatching). We identify 67 distinct cell clusters based on the patterns of gene expression. These include 31 functional mature larval neuron clusters, 1 ring gland cluster, 8 glial clusters, 6 neural precursor clusters, and 13 developing immature adult neuron clusters. Some clusters are present across all stages of larval development, while others are stage specific (such as developing adult neurons). We identify genes that are differentially expressed in each cluster, as well as genes that are differentially expressed at distinct stages of larval life. These differentially expressed genes provide promising candidates for regulating the function of specific neuronal and glial types in the larval nervous system, or the specification and differentiation of adult neurons. The cell transcriptome Atlas of the Drosophila larval nervous system is a valuable resource for developmental biology and systems neuroscience and provides a basis for elucidating how genes regulate neural development and function.
In the fruit fly Drosophila melanogaster, as in mammals, acute exposure to a high dose of ethanol leads to stereotypical behavioral changes beginning with increased activity, followed by incoordination, loss of postural control, and eventually, sedation. The mechanism(s) by which ethanol impacts the CNS leading to ethanol-induced sedation and the genes required for normal sedation sensitivity remain largely unknown. Here we identify the gene apontic (apt), an Myb/SANT-containing transcription factor that is required in the nervous system for normal sensitivity to ethanol sedation. Using genetic and behavioral analyses, we show that apt mediates sensitivity to ethanol sedation by acting in a small set of neurons that express Corazonin (Crz), a neuropeptide likely involved in the physiological response to stress. The activity of Crz neurons regulates the behavioral response to ethanol, as silencing and activating these neurons affects sedation sensitivity in opposite ways. Furthermore, this effect is mediated by Crz, as flies with reduced crz expression show reduced sensitivity to ethanol sedation. Finally, we find that both apt and crz are rapidly upregulated by acute ethanol exposure. Thus, we have identified two genes and a small set of peptidergic neurons that regulate sensitivity to ethanol-induced sedation. We propose that Apt regulates the activity of Crz neurons and/or release of the neuropeptide during ethanol exposure.
In the Drosophila model of aggression, males and females fight in same-sex pairings, but a wide disparity exists in the levels of aggression displayed by the 2 sexes. A screen of Drosophila Flylight Gal4 lines by driving expression of the gene coding for the temperature sensitive dTRPA1 channel, yielded a single line (GMR26E01-Gal4) displaying greatly enhanced aggression when thermoactivated. Targeted neurons were widely distributed throughout male and female nervous systems, but the enhanced aggression was seen only in females. No effects were seen on female mating behavior, general arousal, or male aggression. We quantified the enhancement by measuring fight patterns characteristic of female and male aggression and confirmed that the effect was female-specific. To reduce the numbers of neurons involved, we used an intersectional approach with our library of enhancer trap flp-recombinase lines. Several crosses reduced the populations of labeled neurons, but only 1 cross yielded a large reduction while maintaining the phenotype. Of particular interest was a small group (2 to 4 pairs) of neurons in the approximate position of the pC1 cluster important in governing male and female social behavior. Female brains have approximately 20 doublesex (dsx)-expressing neurons within pC1 clusters. Using dsxFLP instead of 357FLP for the intersectional studies, we found that the same 2 to 4 pairs of neurons likely were identified with both. These neurons were cholinergic and showed no immunostaining for other transmitter compounds. Blocking the activation of these neurons blocked the enhancement of aggression.
Summary Multiple division cycles without growth are a characteristic feature of early embryogenesis. The female germline loads proteins and RNAs into oocytes to support these divisions, which lack many quality control mechanisms operating in somatic cells undergoing growth. Here, we describe a small RNA-Argonaute pathway that ensures early embryonic divisions in C. elegans by employing catalytic slicing activity to broadly tune, instead of silence, germline gene expression. Misregulation of one target, a kinesin-13 microtubule depolymerase, underlies a major phenotype associated with pathway loss. Tuning of target transcript levels is guided by the density of homologous small RNAs, whose generation must ultimately be related to target sequence. Thus, the tuning action of a small RNA-catalytic Argonaute pathway generates oocytes capable of supporting embryogenesis. We speculate that the specialized nature of germline chromatin led to the emergence of small RNA-catalytic Argonaute pathways in the female germline as a post-transcriptional control layer to optimize oocyte composition.
We show that a small subset of two to six subesophageal neurons, expressing the male products of the male courtship master regulator gene products fruitlessMale (fruM), are required in the early stages of the Drosophila melanogaster male courtship behavioral program. Loss of fruM expression or inhibition of synaptic transmission in these fruM(+) neurons results in delayed courtship initiation and a failure to progress to copulation primarily under visually-deficient conditions. We identify a fruM-dependent sexually dimorphic arborization in the tritocerebrum made by two of these neurons. Furthermore, these SOG neurons extend descending projections to the thorax and abdominal ganglia. These anatomical and functional characteristics place these neurons in the position to integrate gustatory and higher-order signals in order to properly initiate and progress through early courtship.
We describe a methodology for rapid experimentation in statistical machine translation which we use to add a large number of features to a baseline system exploiting features from a wide range of levels of syntactic representation. Feature values were combined in a log-linear model to select the highest scoring candidate translation from an n-best list. Feature weights were optimized directly against the BLEU evaluation metric on held-out data. We present results for a small selection of features at each level of syntactic representation.