Filter
Associated Lab
- Aguilera Castrejon Lab (15) Apply Aguilera Castrejon Lab filter
- Ahrens Lab (57) Apply Ahrens Lab filter
- Aso Lab (39) Apply Aso Lab filter
- Baker Lab (38) Apply Baker Lab filter
- Betzig Lab (111) Apply Betzig Lab filter
- Beyene Lab (10) Apply Beyene Lab filter
- Bock Lab (17) Apply Bock Lab filter
- Branson Lab (48) Apply Branson Lab filter
- Card Lab (40) Apply Card Lab filter
- Cardona Lab (63) Apply Cardona Lab filter
- Chklovskii Lab (13) Apply Chklovskii Lab filter
- Clapham Lab (13) 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 (46) Apply Dudman Lab filter
- Eddy/Rivas Lab (30) Apply Eddy/Rivas Lab filter
- Egnor Lab (11) Apply Egnor Lab filter
- Espinosa Medina Lab (16) Apply Espinosa Medina Lab filter
- Feliciano Lab (6) Apply Feliciano Lab filter
- Fetter Lab (41) Apply Fetter Lab filter
- Fitzgerald Lab (28) Apply Fitzgerald Lab filter
- Freeman Lab (15) Apply Freeman Lab filter
- Funke Lab (35) Apply Funke Lab filter
- Gonen Lab (91) Apply Gonen Lab filter
- Grigorieff Lab (62) Apply Grigorieff Lab filter
- Harris Lab (59) Apply Harris Lab filter
- Heberlein Lab (94) Apply Heberlein Lab filter
- Hermundstad Lab (23) Apply Hermundstad Lab filter
- Hess Lab (72) Apply Hess Lab filter
- Ilanges Lab (1) Apply Ilanges Lab filter
- Jayaraman Lab (44) 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 (75) Apply Keller Lab filter
- Koay Lab (17) Apply Koay Lab filter
- Lavis Lab (138) Apply Lavis Lab filter
- Lee (Albert) Lab (34) Apply Lee (Albert) Lab filter
- Leonardo Lab (23) Apply Leonardo Lab filter
- Li Lab (27) Apply Li Lab filter
- Lippincott-Schwartz Lab (161) Apply Lippincott-Schwartz Lab filter
- Liu (Yin) Lab (5) Apply Liu (Yin) Lab filter
- Liu (Zhe) Lab (61) Apply Liu (Zhe) Lab filter
- Looger Lab (137) 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 (6) Apply O'Shea Lab filter
- Otopalik Lab (13) Apply Otopalik Lab filter
- Pachitariu Lab (42) Apply Pachitariu Lab filter
- Pastalkova Lab (18) Apply Pastalkova Lab filter
- Pavlopoulos Lab (19) Apply Pavlopoulos Lab filter
- Pedram Lab (14) Apply Pedram Lab filter
- Podgorski Lab (16) Apply Podgorski Lab filter
- Reiser Lab (49) Apply Reiser Lab filter
- Riddiford Lab (44) Apply Riddiford Lab filter
- Romani Lab (41) Apply Romani Lab filter
- Rubin Lab (139) Apply Rubin Lab filter
- Saalfeld Lab (61) Apply Saalfeld Lab filter
- Satou Lab (16) Apply Satou Lab filter
- Scheffer Lab (36) Apply Scheffer Lab filter
- Schreiter Lab (62) Apply Schreiter Lab filter
- Sgro Lab (20) Apply Sgro Lab filter
- Shroff Lab (24) Apply Shroff Lab filter
- Simpson Lab (23) Apply Simpson Lab filter
- Singer Lab (80) Apply Singer Lab filter
- Spruston Lab (91) Apply Spruston Lab filter
- Stern Lab (152) Apply Stern Lab filter
- Sternson Lab (54) Apply Sternson Lab filter
- Stringer Lab (29) Apply Stringer Lab filter
- Svoboda Lab (135) Apply Svoboda Lab filter
- Tebo Lab (31) Apply Tebo Lab filter
- Tervo Lab (9) Apply Tervo Lab filter
- Tillberg Lab (17) Apply Tillberg Lab filter
- Tjian Lab (64) Apply Tjian Lab filter
- Truman Lab (88) Apply Truman Lab filter
- Turaga Lab (46) Apply Turaga Lab filter
- Turner Lab (35) Apply Turner Lab filter
- Vale Lab (6) Apply Vale Lab filter
- Voigts Lab (2) Apply Voigts Lab filter
- Wang (Meng) Lab (10) Apply Wang (Meng) Lab filter
- Wang (Shaohe) Lab (24) 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 (6) Apply CellMap filter
- COSEM (3) Apply COSEM filter
- Fly Descending Interneuron (10) Apply Fly Descending Interneuron filter
- Fly Functional Connectome (14) Apply Fly Functional Connectome filter
- Fly Olympiad (5) Apply Fly Olympiad filter
- FlyEM (51) Apply FlyEM filter
- FlyLight (46) Apply FlyLight filter
- GENIE (40) Apply GENIE filter
- Integrative Imaging (1) Apply Integrative Imaging filter
- Larval Olympiad (2) Apply Larval Olympiad filter
- MouseLight (16) Apply MouseLight filter
- NeuroSeq (1) Apply NeuroSeq filter
- ThalamoSeq (1) Apply ThalamoSeq filter
- Tool Translation Team (T3) (24) Apply Tool Translation Team (T3) filter
- Transcription Imaging (49) Apply Transcription Imaging filter
Publication Date
- 2024 (172) Apply 2024 filter
- 2023 (171) Apply 2023 filter
- 2022 (192) Apply 2022 filter
- 2021 (193) 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
3947 Publications
Showing 2751-2760 of 3947 resultsImage stability during self motion depends on the combined actions of the vestibuloocular and optokinetic reflexes (VOR and OKR, respectively). Neurons in the medial vestibular nucleus (MVN) participate in the VOR and OKR by firing in response to both head and image motion. Their intrinsic spike-generating properties enable MVN neurons to modulate firing rates linearly over a broad range of input amplitudes and frequencies such as those that occur during natural head and image motion. This study examines the postnatal development of the intrinsic spike-generating properties of rat MVN neurons with respect to maturation of peripheral vestibular and visual function. Spike generation was studied in a brain stem slice preparation by recording firing responses to current injected intracellularly through whole cell patch electrodes. MVN neurons fired spontaneously and modulated their firing rate in response to injected current at all postnatal ages. However, the input-output properties of the spike generator changed dramatically during the first two postnatal weeks. Neurons younger than postnatal day 10 could not fire faster than 80 spikes/s, modulated their firing rates over a limited range of input amplitudes, and tended to exhibit a nonlinear relationship between input current and mean evoked firing rate. In response to sustained depolarization, firing rates declined significantly in young neurons. Response gains tended to be highest in the first few postnatal days but varied widely across neurons and were not correlated with age. By about the beginning of the third postnatal week, MVN neurons could fire faster than 100 spikes/s in response to a broad range of input amplitudes, exhibited predominantly linear current-firing rate relationships, and adapted little in response to sustained depolarization. Concomitant decreases in action potential width and the time course of the afterhyperpolarization suggest that changes in potassium currents contribute to the maturation of the MVN neuronal spike generator. The results demonstrate that developmental changes in intrinsic membrane properties enable MVN neurons to fire linearly in response to a broad range of stimuli in time for the onset of visual function at the beginning of the third postnatal week.
Long-term potentiation and long-term depression require postsynaptic depolarization, which many current models attribute to backpropagating action potentials. New experimental work suggests, however, that other mechanisms can lead to dendritic depolarization, and that backpropagating action potentials may be neither necessary nor sufficient for synaptic plasticity in vivo.
Active dendritic synaptic integration enhances the computational power of neurons. Such nonlinear processing generates an object-localization signal in the apical dendritic tuft of layer 5B cortical pyramidal neurons during sensory-motor behavior. Here, we employ electrophysiological and optical approaches in brain slices and behaving animals to investigate how excitatory synaptic input to this distal dendritic compartment influences neuronal output. We find that active dendritic integration throughout the apical dendritic tuft is highly compartmentalized by voltage-gated potassium (KV) channels. A high density of both transient and sustained KV channels was observed in all apical dendritic compartments. These channels potently regulated the interaction between apical dendritic tuft, trunk, and axosomatic integration zones to control neuronal output in vitro as well as the engagement of dendritic nonlinear processing in vivo during sensory-motor behavior. Thus, KV channels dynamically tune the interaction between active dendritic integration compartments in layer 5B pyramidal neurons to shape behaviorally relevant neuronal computations.
The pea aphid, Acyrthosiphon pisum, exhibits several environmentally cued, discrete, alternate phenotypes (polyphenisms) during its life cycle. In the case of the reproductive polyphenism, differences in day length determine whether mothers will produce daughters that reproduce either sexually by laying fertilized eggs (oviparous sexual reproduction), or asexually by allowing oocytes to complete embryogenesis within the mother without fertilization (viviparous parthenogenesis). Oocytes and embryos that are produced asexually develop more rapidly, are yolk-free, and much smaller than oocytes and embryos that are produced sexually. Perhaps most striking, the process of oocyte differentiation is truncated in the case of asexual/viviparous development, potentially precluding interactions between the oocyte and surrounding follicle cells that might take place during sexual/oviparous development. Given the important patterning roles that oocyte-follicle cell interactions play in Drosophila, these overt differences suggest that there may be underlying differences in the molecular mechanisms of pattern formation. We have found differences in the expression of homologs of torso-like and tailless, as well as activated MAP kinase, suggesting that there are important differences in the hemipteran version of the terminal patterning system between viviparous and oviparous development. Establishing such differences in the expression of patterning genes between these developmental modes is a first step toward understanding how a single genome manages to direct patterning events in such different embryological contexts.
Axon calibers vary widely among different animals, neuron classes, and even within the same neuron. What determines the diameter of axon branches?
Fluorescence microscopy has evolved from a purely observational tool to a platform for quantitative, hypothesis-driven research. As such, the demand for faster and less phototoxic imaging modalities has spurred a rapid growth in light sheet fluorescence microscopy (LSFM). By restricting the excitation to a thin plane, LSFM reduces the overall light dose to a specimen while simultaneously improving image contrast. However, the defining characteristics of light sheet microscopes subsequently warrant unique considerations in their use for quantitative experiments. In this Perspective, we outline many of the pitfalls in LSFM that can compromise analysis and confound interpretation. Moreover, we offer guidance in addressing these caveats when possible. In doing so, we hope to provide a useful resource for life scientists seeking to adopt LSFM to quantitatively address complex biological hypotheses.
The rapid advancement of live-cell imaging technologies has enabled biologists to generate high-dimensional data to follow biological movement at the microscopic level. Yet, the "perceived" ease of use of modern microscopes has led to challenges whereby sub-optimal data are commonly generated that cannot support quantitative tracking and analysis as a result of various ill-advised decisions made during image acquisition. Even optimally acquired images often require further optimization through digital processing before they can be analyzed. In writing this article, we presume our target audience to be biologists with a foundational understanding of digital image acquisition and processing, who are seeking to understand the essential steps for particle/object tracking experiments. It is with this targeted readership in mind that we review the basic principles of image-processing techniques as well as analysis strategies commonly used for tracking experiments. We conclude this technical survey with a discussion of how movement behavior can be mathematically modeled and described. © 2019 by John Wiley & Sons, Inc.
A large variability in performance is observed when participants recall briefly presented lists of words. The sources of such variability are not known. Our analysis of a large data set of free recall revealed a small fraction of participants that reached an extremely high performance, including many trials with the recall of complete lists. Moreover, some of them developed a number of consistent input-position-dependent recall strategies, in particular recalling words consecutively ("chaining") or in groups of consecutively presented words ("chunking"). The time course of acquisition and particular choice of positional grouping were variable among participants. Our results show that acquiring positional strategies plays a crucial role in improvement of recall performance.
The major site of in vivo transcriptional initiation for both heavy and light strands of human mitochondrial DNA is the displacement-loop region. Transcripts synthesized in vitro by human mitochondrial RNA polymerase were mapped to the nucleotide level and have identical 5’ end map positions to those reported for in vivo primary transcripts. An ordered series of deletion clones, whose template sequences were truncated at either the 5’ or 3’ end, was used to identify the precise mitochondrial DNA sequence required for initiation of transcription. The data provide a definitive assignment of the promoter for heavy-strand transcription occurring within -16 to +7 of the transcriptional start site 16 nucleotides upstream of the 5’ end of the gene for tRNAPhe and of the promoter for light-strand transcription occurring within -28 to +16 of the transcriptional start site at the 5’ end of "7S RNA." Within each control sequence is a candidate promoter whose consensus sequence is 5’-CANACC(G)CC(A)AAAGAPyA-3’ and in both cases transcriptional initiation occurs within six to eight nucleotides of the 3’ end of this sequence. The transcriptional start site is an integral part of each promoter and each promoter can function in the absence of the other.
In terrestrial vertebrates, sniffing controls odorant access to receptors, and therefore sets the timescale of olfactory stimuli. We found that odorants evoked precisely sniff-locked activity in mitral/tufted cells in the olfactory bulb of awake mouse. The trial-to-trial response jitter averaged 12 ms, a precision comparable to other sensory systems. Individual cells expressed odor-specific temporal patterns of activity and, across the population, onset times tiled the duration of the sniff cycle. Responses were more tightly time-locked to the sniff phase than to the time after inhalation onset. The spikes of single neurons carried sufficient information to discriminate odors. In addition, precise locking to sniff phase may facilitate ensemble coding by making synchrony relationships across neurons robust to variation in sniff rate. The temporal specificity of mitral/tufted cell output provides a potentially rich source of information for downstream olfactory areas.