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5,448 results for “Neuron”
Data from: A descending neuron correlated with the rapid steering maneuvers of flying Drosophila
To navigate through the world, animals must stabilize their path against disturbances and change direction to avoid obstacles and to search for resources. Locomotion is thus guided by sensory cues but also depends on intrinsic processes, such as motivation and physiological state. Flies, for example, turn with the direction of large-field rotatory motion, an optomotor reflex that is thought to help them fly straight. Occasionally, however, they execute fast turns, called body saccades, either spontaneously or in response to patterns of visual motion such as expansion. These turns can be measured in tethered flying Drosophila, which facilitates the study of underlying neural mechanisms. Whereas there is evidence for an efference copy input to visual interneurons during saccades, the circuits that control spontaneous and visually elicited saccades are not well known. Using two-photon calcium imaging and electrophysiological recordings in tethered flying Drosophila, we have identified a descending neuron whose activity is correlated with both spontaneous and visually elicited turns during tethered flight. The cell's activity in open- and closed-loop experiments suggests that it does not underlie slower compensatory responses to horizontal motion but rather controls rapid changes in flight path. The activity of this neuron can explain some of the behavioral variability observed in response to visual motion and appears sufficient for eliciting turns when artificially activated. This work provides an entry point into studying the circuits underlying the control of rapid steering maneuvers in the fly brain.
Data from: Artificial selection on brain size leads to matching changes in overall number of neurons
Neurons are the basic computational units of the brain, but brain size is the predominant surrogate measure of brain functional capacity in comparative and cognitive neuroscience. This approach is based on the assumption that larger brains harbour higher numbers of neurons and their connections and therefore have a higher information processing capacity. However, recent studies have shown that brain mass may be less strongly correlated with neuron counts than previously thought. Till now, no experimental test has been conducted to examine the relationship between evolutionary changes in brain size and the number of brain neurons. Here we provide such a test by comparing neuron number in artificial selection lines of female guppies (Poecilia reticulata) with >15% difference in relative brain mass and numerous previously demonstrated cognitive differences. Using the isotropic fractionator, we demonstrate that large-brained females have a higher overall number of neurons than small-brained females, but similar neuronal densities. Importantly, this difference holds also for the telencephalon, a key region for cognition. Our study provides the first direct experimental evidence that selection for brain mass leads to matching changes in number of neurons and shows that brain size evolution is intimately linked to the evolution of neuron number and cognition.
Data from: Visual landmarks sharpen grid cell metric and confer context specificity to neurons of the medial entorhinal cortex
Neurons of the medial entorhinal cortex (MEC) provide spatial representations critical for navigation. In this network, the periodic firing fields of grid cells act as a metric element for position. The location of the grid firing fields depends on interactions between self-motion information, geometrical properties of the environment and nonmetric contextual cues. Here, we test whether visual information, including nonmetric contextual cues, also regulates the firing rate of MEC neurons. Removal of visual landmarks caused a profound impairment in grid cell periodicity. Moreover, the speed code of MEC neurons changed in darkness and the activity of border cells became less confined to environmental boundaries. Half of the MEC neurons changed their firing rate in darkness. Manipulations of nonmetric visual cues that left the boundaries of a 1D environment in place caused rate changes in grid cells. These findings reveal context specificity in the rate code of MEC neurons.
Data from: Optogenetic recruitment of dorsal raphe serotonergic neurons acutely decreases mechanosensory responsivity in behaving mice
The inhibition of sensory responsivity is considered a core serotonin function, yet this hypothesis lacks direct support due to methodological obstacles. We adapted an optogenetic approach to induce acute, robust and specific firing of dorsal raphe serotonergic neurons. In vitro, the responsiveness of individual dorsal raphe serotonergic neurons to trains of light pulses varied with frequency and intensity as well as between cells, and the photostimulation protocol was therefore adjusted to maximize their overall output rate. In vivo, the photoactivation of dorsal raphe serotonergic neurons gave rise to a prominent light-evoked field response that displayed some sensitivity to a 5-HT1A agonist, consistent with autoreceptor inhibition of raphe neurons. In behaving mice, the photostimulation of dorsal raphe serotonergic neurons produced a rapid and reversible decrease in the animals' responses to plantar stimulation, providing a new level of evidence that serotonin gates sensory-driven responses.
Data from: In-situ recording of ionic currents in projection neurons and Kenyon cells in the olfactory pathway of the honeybee
The honeybee olfactory pathway comprises an intriguing pattern of convergence and divergence: ~60.000 olfactory sensory neurons (OSN) convey olfactory information on ~900 projection neurons (PN) in the antennal lobe (AL). To transmit this information reliably, PNs employ relatively high spiking frequencies with complex patterns. PNs project via a dual olfactory pathway to the mushroom bodies (MB). This pathway comprises the medial (m-ALT) and the lateral antennal lobe tract (l-ALT). PNs from both tracts transmit information from a wide range of similar odors, but with distinct differences in coding properties. In the MBs, PNs form synapses with many Kenyon cells (KC) that encode odors in a spatially and temporally sparse way. The transformation from complex information coding to sparse coding is a well-known phenomenon in insect olfactory coding. Intrinsic neuronal properties as well as GABAergic inhibition are thought to contribute to this change in odor representation. In the present study, we identified intrinsic neuronal properties promoting coding differences between PNs and KCs using in-situ patch-clamp recordings in the intact brain. We found very prominent K+ currents in KCs clearly differing from the PN currents. This suggests that odor coding differences between PNs and KCs may be caused by differences in their specific ion channel properties. Comparison of ionic currents of m- and l-ALT PNs did not reveal any differences at a qualitative level.
Neuron Fractal Image Reading
<p>Seed along with few iterations of neuron fractal has been provided.</p>
Gut Analysis Toolbox: Training data and 2D models for segmenting enteric neurons, neuronal subtypes and ganglia
<p>This upload is associated with the software, <a href="https://github.com/pr4deepr/GutAnalysisToolbox">Gut Analysis Toolbox</a> (GAT).</p> <p>If you use it please cite:</p> <p><strong><em>Sorensen et al. Gut Analysis Toolbox: Automating quantitative analysis of enteric neurons. J Cell Sci 2024; jcs.261950. doi: <a href="https://doi.org/10.1242/jcs.261950" target="_blank" rel="noopener">https://doi.org/10.1242/jcs.261950</a></em></strong></p> <p>The upload contains<strong> StarDist models for segmenting enteric neurons in 2D, enteric neuronal subtypes in 2D and FPN+ResNet101 model for enteric ganglia in 2D in gut wholemount tissue.</strong> GAT is implemented in Fiji, but the models can be used in any software that supports StarDist and the use of 2D UNet models. The files here also consist of <strong>Python notebooks (Google Colab)</strong>, training and test data as well as reports on model performance.</p> <p>Note: The enteric ganglia model is has been updated to v3 which uses pytorch and is a different architecture (FPN+ResNet101).</p> <p>The model files are located in the respective folders as zip files. The folders have also been zipped:</p> <ul> <li>Neuron (Hu; <a href="https://github.com/stardist/stardist">StarDist</a> model): <ul> <li>Main folder: 2D_enteric_neuron_model_QA.zip</li> <li>StarDist Model File:2D_enteric_neuron_v4_1.zip </li> <li>DeepImageJ compatible model: 2D_enteric_neuron.bioimage.io.model.zip (used currently in GAT)</li> </ul> </li> <li>Neuronal subtype (<a href="https://github.com/stardist/stardist">StarDist</a> model): <ul> <li>Main folder: 2D_enteric_neuron_subtype_model_QA.zip</li> <li>Model File: 2D_enteric_neuron_subtype_v4.zip</li> <li>DeepImageJ compatible model: 2D_enteric_neuron_subtype.bioimage.io.model.zip (used currently in GAT)</li> </ul> </li> <li>Enteric ganglia (2D FPN_ResNet101; Use in FIJI with <a href="https://deepimagej.github.io/deepimagej/">deepImageJ</a>) <ul> <li>Main folder: 2D_enteric_ganglia_v3_training.zip</li> <li>Model File: 2D_Ganglia_RGB_v3.bioimage.io.model.zip (used currently in GAT)</li> </ul> </li> </ul> <p>For the all models, files included are:</p> <ol> <li>Model for segmenting cells or ganglia in 2D FIJI. StarDist or 2D UNet.</li> <li>Training and Test datasets used for training.</li> <li>Google Colab notebooks used for training and quality assurance (<a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">ZeroCost DL4Mic notebooks</a>).</li> <li>Python notebook and code for training ganglia model with QA.</li> <li>Quality assurance reports generated from above notebooks.</li> <li>StarDist model exported for use in QuPath.</li> </ol> <p>The model files can be used within can be used within the software, <a href="https://github.com/stardist/stardist">StarDist</a>. They are intended to be used within FIJI or QuPath, but can be used in any software that supports the implementation of StarDist in 2D.</p> <p><strong>Data:</strong></p> <p>All the images were collected from 4 different research labs and a public database (<a href="https://sparc.science/data?type=dataset">SPARC database</a>) to account for variations in image acquisition, sample preparation and immunolabelling.</p> <p>For enteric neurons the pan-neuronal marker, Hu has been used and the 2D wholemounts images from mouse, rat and human tissue.</p> <p>For enteric neuronal subtypes, 2D images for nNOS, MOR, DOR, ChAT, Calretinin, Calbindin, Neurofilament, CGRP and SST from mouse tissue have been used..</p> <p>25 images were used from the following entries in the <a href="https://sparc.science/data?type=dataset">SPARC database</a>:</p> <ul> <li><a href="https://doi.org/10.26275/9FFG-482D">Howard, M. (2021). 3D imaging of enteric neurons in mouse (Version 1) [Data set]. SPARC Consortium. </a></li> <li><a href="https://doi.org/10.26275/PZEK-91WX">Graham, K. D., Huerta-Lopez, S., Sengupta, R., Shenoy, A., Schneider, S., Wright, C. M., Feldman, M., Furth, E., Lemke, A., Wilkins, B. J., Naji, A., Doolin, E., Howard, M., & Heuckeroth, R. (2020). Robust 3-Dimensional visualization of human colon enteric nervous system without tissue sectioning (Version 1) [Data set]. SPARC Consortium.</a></li> <li>Wang, L., Yuan, P.-Q., Gould, T. and Tache, Y. (2021). Antibodies Tested in theColon – Mouse (Version 1) [Data set]. SPARC Consortium. doi:10.26275/i7dl-58h</li> </ul> <p>Additional images for new ganglia model:</p> <ul> <li>Hamnett, R., Dershowitz, L. B., Sampathkumar, V., Wang, Z., Gomez-Frittelli, J., De Andrade, V., Kasthuri, N., Druckmann, S. and Kaltschmidt, J. A. (2022b). Regional cytoarchitecture of the adult and developing mouse enteric nervous system. Curr. Biol. 32, 4483-4492.e5.</li> </ul> <p>The images have been acquired using a combination different microscopes. The images for the mouse tissue were acquired using: </p> <ul> <li> <p>Leica TCS-SP8 confocal system (20x HC PL APO NA 1.33, 40 x HC PL APO NA 1.3) </p> </li> <li> <p>Leica TCS-SP8 lightning confocal system (20x HC PL APO NA 0.88) </p> </li> <li> <p>Zeiss Axio Imager M2 (20X HC PL APO NA 0.3) </p> </li> <li> <p>Zeiss Axio Imager Z1 (10X HC PL APO NA 0.45) </p> </li> </ul> <p>Human tissue images were acquired using: </p> <ul> <li> <p>IX71 Olympus microscope (10X HC PL APO NA 0.3) </p> </li> </ul> <p>For more information, visit the <a href="https://gut-analysis-toolbox.gitbook.io/docs" target="_blank" rel="noopener">Documentation</a> website.</p> <p><strong>NOTE:</strong> The images for enteric neurons and neuronal subtypes have been rescaled to 0.568 µm/pixel for mouse and rat. For human neurons, it has been rescaled to 0.9 µm/pixel . This is to ensure the neuronal cell bodies have similar pixel area across images. The area of cells in pixels can vary based on resolution of image, magnification of objective used, animal species (larger animals -> larger neurons) and potentially how the tissue is stretched during wholemount preparation </p> <p>Average neuron area for neuronal model: 701.2 ± 195.9 pixel<sup>2 </sup>(Mean ± SD, 6267 cells)</p> <p>Average neuron area for neuronal subtype model: 880.9 ± 316 pixel<sup>2 </sup>(Mean ± SD, 924 cells)</p> <p><strong>Software References:</strong></p> <p><strong><a href="https://github.com/stardist/stardist">Stardist</a></strong></p> <p>Schmidt, U., Weigert, M., Broaddus, C., & Myers, G. (2018, September). Cell detection with star-convex polygons. In <em>International Conference on Medical Image Computing and Computer-Assisted Intervention</em> (pp. 265-273). Springer, Cham.</p> <p><strong><a href="https://deepimagej.github.io/deepimagej/">deepImageJ</a></strong></p> <p>Gómez-de-Mariscal, E., García-López-de-Haro, C., Ouyang, W., Donati, L., Lundberg, E., Unser, M., Muñoz-Barrutia, A. and Sage, D., 2021. DeepImageJ: A user-friendly environment to run deep learning models in ImageJ. <em>Nature Methods</em>, <em>18</em>(10), pp.1192-1195.</p> <p><strong><a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">ZeroCost DL4Mic</a></strong></p> <p>von Chamier, L., Laine, R.F., Jukkala, J., Spahn, C., Krentzel, D., Nehme, E., Lerche, M., Hernández-Pérez, S., Mattila, P.K., Karinou, E. and Holden, S., 2021. Democratising deep learning for microscopy with ZeroCostDL4Mic. <em>Nature communications</em>, <em>12</em>(1), pp.1-18.</p>
Combinatorial expression of neurexin genes regulates glomerular targeting by olfactory sensory neurons
<p>This deposit contains the MERFISH data used in this study.</p>
RAW DATA for "Kalium channelrhodopsins effectively inhibit neurons in the small model animals"
Open the record for dataset details and reuse information.
Codes and images associated with "Neuronal dynamics orchestrate cerebrospinal fluid perfusion and brain clearance"
<p>MATLAB scripts and representative images associated with the publication. </p>
Biologically plausible information propagation in a complementary metal-oxide semiconductor integrate-and-fire artificial neuron circuit with memristive synapses
<p>This is the repository containing the datasets relative to the publication: <em>L. Benatti, T. Zanotti, D. Gandolfi, J. Mapelli, and F. M. Puglisi, “Biologically plausible information propagation in a complementary metal-oxide semiconductor integrate-and-fire artificial neuron circuit with memristive synapses,” Nano Futures, vol. 7, no. 2, p. 025003, May 2023, doi: <a href="https://doi.org/10.1088/2399-1984/accf53">10.1088/2399-1984/accf53</a>.</em></p> <p>In this folder, you will find MATLAB workspaces containing:</p> <ul> <li><strong>Sni:</strong> Tables with stimuli values (binary and decimal), noise, and surprise, divided by synapse conductance values.</li> <li><strong>MI_vs_G:</strong> Vectors containing simulated values of mutual information (MI), noise, and entropy associated with synapse conductance values, as shown in Figure 5.</li> <li><strong>SpS_vs_rank:</strong> Traces of surprise per spike (SpS) related to synapse conductance values, ordered by stimulus rank. <br> This includes the tracking of stimuli associated with lower/higher SpS as synapse conductance varies, as illustrated in Figure 6b.</li> </ul>
Figure 4 from: Nugroho A, Tamtomo DG, Indarto D, Cilmiaty R, Soetrisno (2024) The effect of L-Arginine from Giant Snake Head fish (Channa micropeltes) on neuroinflammation and neuron damage in traumatic brain injury in rats. Pharmacia 71: 1-9. https://doi.org/10.3897/pharmacia.71.e111239
Figure 4 Immunohistochemical staining of Caspase-3 400× microscope magnification. Picture of caspase 3 expression in the cerebral cortex area shows a picture of caspase 3 expression in the cytoplasm of neuron cells (yellow arrow). A. The normal control group showed a score of 0; B. The negative control group shows a score of 4; C. Group A shows a score of 3; D. Group B shows a score of 2; E. Group C shows a score of 1.
Figure 5 from: Nugroho A, Tamtomo DG, Indarto D, Cilmiaty R, Soetrisno (2024) The effect of L-Arginine from Giant Snake Head fish (Channa micropeltes) on neuroinflammation and neuron damage in traumatic brain injury in rats. Pharmacia 71: 1-9. https://doi.org/10.3897/pharmacia.71.e111239
Figure 5 Histopathological picture of brain tissue damage with HE staining, 400× microscope magnification. Description: Histopathological picture of the cerebral cortex area shows degeneration of neuron cells (yellow arrows). A. The normal control group shows a score of 0; B. The negative control group shows a score of 2; C. Group A shows a score of 2; D. Group B shows a score of 1; E. Group C shows a score of 1.
Figure 2 from: Nugroho A, Tamtomo DG, Indarto D, Cilmiaty R, Soetrisno (2024) The effect of L-Arginine from Giant Snake Head fish (Channa micropeltes) on neuroinflammation and neuron damage in traumatic brain injury in rats. Pharmacia 71: 1-9. https://doi.org/10.3897/pharmacia.71.e111239
Figure 2 TLR4 immunohistochemical staining with a microscope magnification of 400×. Description: TLR4 expression in the cerebral cortex area shows TLR4 expression in astrocytes (yellow arrows). A. Control group normally expressed 5%; B. Negative control group expressed 20%; C. Group A expressed 15%; D. Group B expressed 15%; E. Group C expressed 10%.
Figure 3 from: Nugroho A, Tamtomo DG, Indarto D, Cilmiaty R, Soetrisno (2024) The effect of L-Arginine from Giant Snake Head fish (Channa micropeltes) on neuroinflammation and neuron damage in traumatic brain injury in rats. Pharmacia 71: 1-9. https://doi.org/10.3897/pharmacia.71.e111239
Figure 3 TNF-α immunohistochemical staining with a microscope magnification of 400×. TNF-α expression in the cerebral cortex area shows TNF-α expression in astrocytes (yellow arrows). A. Control group normally expressed 5%; B. Negative control group expressed 15%; C. Group A was depressed 10%; D. Group B expressed 10%; E. Group C expressed 5%.
Parkinsonism Sac domain mutation in Synaptojanin-1 affects ciliary properties in iPSC-derived dopaminergic neurons
<p>The tabular dataset for all graphs in Rafiq et al 2024 can be found in the ASAP data repository</p>
Fig 6 ChR2 50 s open loop, in Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla
<p>Sleep recording data for ChR2 50 s open loop experiments. Associated with Figure 6.</p>
Fig S5 AAVrg-ChR2 closed loop, in Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla
<p>Sleep recording data for AAVrg closed loop experiments. Associated with Figure S5.</p>
Fig 1 eYFP open loop, in Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla
<p>Sleep recordings for eYFP open loop control data. Associated with Figure 1.</p>
Fig 2 Swichr++ closed loop, in Regulation of REM Sleep by Inhibitory Neurons in the Dorsomedial Medulla
<p>Sleep recording data for swichr++ closed loop experiments. Associated with Figure 2D.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.