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17 results for “receptive fields”
Dataset for manuscript 'CeyeHao: AI-driven microfluidic flow programming with hierarchically assembled obstacles in microchannel and receptive-field-augmented neural network'
<p>This dataset contains:<br>1. The dataset used to train the models related to the manuscript 'CeyeHao: AI-driven microfluidic flow programming using hierarchically assembled obstacles in microchannel with receptive-field-augmented neural network'.<br>2. A checkpoint of trained 'CEyeNet' proposed in the manuscript.<br>3. Example microchannels designed in the manuscript to produce semantic flow profiles</p> <p>This dataset is intended for research and academic purpose.</p> <p>Detailed description please refer to the enclosed ReadMe.txt.</p>
Dual receptive fields underlying target and wide-field motion sensitivity in looming sensitive descending neurons
<p><span>Many animals use motion vision information to control dynamic behaviors. Predatory animals, for example, show an exquisite ability to detect rapidly moving prey followed by pursuit and capture. Such target detection is not only used by predators but can also play an important role in conspecific interactions. Male hoverflies (<em>Eristalis</em> <em>tenax</em>), for example, vigorously defend their territories against conspecific intruders. Visual target detection is believed to be subserved by specialized target-tuned neurons that are found in a range of species, including cats, zebrafish, and insects. However, how these target-tuned neurons respond to actual pursuit trajectories is currently not well understood. To redress this, we recorded extracellularly from target selective descending neurons (TSDNs) in male <em>Eristalis</em> <em>tenax</em> hoverflies. We show that the neurons have dorso-frontal receptive fields, with a preferred direction up and away from the visual midline. We next reconstructed visual flow-fields as experienced during pursuits of artificial targets (black beads). We recorded TSDN responses to six reconstructed pursuits and found that each neuron responded consistently at remarkably specific time points, but that these time points differed between neurons. We compared the observed spike probability with the spike probability predicted from each neuron's receptive field and size tuning and found a correlation coefficient of 0.35. Interestingly, however, the overall response rate was low, with individual neurons responding to only a small part of each reconstructed pursuit. In contrast, the TSDN population responded to a substantially larger proportion of the pursuits (up to a median of 23%). This large variation between neurons could be useful if different neurons control different parts of the behavioral output.</span></p>
Dual receptive fields underlying target and wide-field motion sensitivity in looming sensitive descending neurons
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Data from: Complexity of frequency receptive fields predicts tonotopic variability across species
<p><span>Primary cortical areas contain maps of sensory features, including sound frequency in primary auditory cortex (A1). Two-photon calcium imaging in mice has confirmed the presence of these global tonotopic maps, while uncovering an unexpected local variability in the stimulus preferences of individual neurons in A1 and other primary regions. Here we show that local heterogeneity of frequency preferences is not unique to rodents. Using two-photon calcium imaging in layers 2/3, we found that local variance in frequency preferences is equivalent in ferrets and mice. Neurons with multipeaked frequency tuning are less spatially organized than those tuned to a single frequency in both species. Furthermore, we show that microelectrode recordings may describe a smoother tonotopic arrangement due to a sampling bias towards neurons with simple frequency tuning. These results help explain previous inconsistencies in cortical topography across species and recording techniques. </span></p>
Intersession reliability of population receptive field estimates
<p>Proccessed pRF data comparing parameter estimates in visual regions across two days. Requires Matlab and SamSrf toolbox (https://figshare.com/articles/SamSrf_toolbox_for_pRF_mapping/1344765).</p>
Understanding the Influence of Receptive Field and Network Complexity in Neural-Network-Guided TEM Image Analysis
<p>TEM images of Au nanoparticles of various sizes on ultra-thin carbon substrates and their corresponding labels for semantic segmentation. The images have a dataset label of "images" and the labels have a dataset label of "labels". </p>
Data from: Complexity of frequency receptive fields predicts tonotopic variability across species
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Data from: Evaluating population receptive field estimation frameworks in terms of robustness and reproducibility
Within vision research retinotopic mapping and the more general receptive field estimation approach constitute not only an active field of research in itself but also underlie a plethora of interesting applications. This necessitates not only good estimation of population receptive fields (pRFs) but also that these receptive fields are consistent across time rather than dynamically changing. It is therefore of interest to maximize the accuracy with which population receptive fields can be estimated in a functional magnetic resonance imaging (fMRI) setting. This, in turn, requires an adequate estimation framework providing the data for population receptive field mapping. More specifically, adequate decisions with regard to stimulus choice and mode of presentation need to be made. Additionally, it needs to be evaluated whether the stimulation protocol should entail mean luminance periods and whether it is advantageous to average the blood oxygenation level dependent (BOLD) signal across stimulus cycles or not. By systematically studying the effects of these decisions on pRF estimates in an empirical as well as simulation setting we come to the conclusion that a bar stimulus presented at random positions and interspersed with mean luminance periods is generally most favorable. Finally, using this optimal estimation framework we furthermore tested the assumption of temporal consistency of population receptive fields. We show that the estimation of pRFs from two temporally separated sessions leads to highly similar pRF parameters.
Intrinsic and synaptic determinants of receptive field plasticity in Purkinje cells of the mouse cerebellum
<p>Here we provide the data for the paper "Intrinsic and synaptic determinants of receptive field plasticity in Purkinje cells of the mouse cerebellum". The dataset is saved as MAT-file (version 7.0). The source code for analysis is saved in github. Folder names indicate the contents corresponding to the specific figures in the paper.</p>
Heterogeneous orientation tuning in primary visual cortex of mice diverges from Gabor-like receptive fields in primates
<div> <h2>Data for the Fu et al. (2024) article: 'Heterogeneous orientation tuning in primary visual cortex of mice diverges from Gabor-like receptive fields in primates'</h2> <p> </p> </div> <div> <h3>Summary</h3> </div> <p>Here we provide the complete data for the article Fu et al., 2024 'Heterogeneous orientation tuning in primary visual cortex of mice diverges from Gabor-like receptive fields in primates': include link.</p> <p>The mouse datasets consists of X individual datasets (i.e. recording scans) of calcium activity of L2/3 and L4 neurons in mouse V1. All datasets were acquired using two-photon imaging of awake, head-fixed mice.</p> <p>The monkey dataset has already been published <a href="https://figshare.com/collections/Monkey_V1_and_V4_single-cell_responses_to_natural_images_ephys_Data_from_Cadena_et_al_2024_/6658331/2">here</a>.</p> <div> <h3>Repository structure</h3> </div> <p>The datasets are divided into different experimental paradigms.</p> <p><strong>Imagenet scans</strong> (starting with "static*.zip": contain the neuronal activity in response to grayscale naturalistic images. We used these scans for training deep convolutional neural networks to learn an <em>in-silico</em> model of the recorded neuronal population and to optimize MEIs as well as optimal Gabors. The file "ImageNet_Data_Structure.md" contains detailed information about the content of the files.</p> <p><strong>Dotmap and orientation scans </strong>("dataset_*.pkl"): These scans include two types of stimuli: 1) A sparse noise paradigm for mapping receptive fields of visual neurons. 2) Small patches of drifting gratings to study the orientation tuning selectivity at sub receptive field scale of mouse V1 neurons. The file "RFMapping_Orientation_Data_Structure.md" contains information about the content of the files.</p> <div> <h3>Related Repositories</h3> </div> <p>We used the following Github repositories for analysis, which are all publicly available:</p> <ul> <li>Processing of the calcium data: <a href="https://github.com/cajal/pipeline">https://github.com/cajal/pipeline</a></li> <li>Model training of mouse datasets: <a href="https://github.com/sinzlab/nnidentify">https://github.com/sinzlab/nnidentify</a></li> <li>MEI optimization: <a href="https://github.com/sinzlab/mei/tree/inception_loop">https://github.com/sinzlab/mei/tree/inception_loop</a></li> <li>Gabor optimization: <a href="https://github.com/mohammadbashiri/fitgabor">https://github.com/mohammadbashiri/fitgabor</a></li> </ul>
Data from: Evaluating population receptive field estimation frameworks in terms of robustness and reproducibility
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Inference of nonlinear receptive field subunits with spike-triggered clustering
<p>Responses of sensory neurons are often modeled using a weighted combination of rectified linear subunits. Since these subunits often cannot be measured directly, a flexible method is needed to infer their properties from the responses of downstream neurons. We present a method for maximum likelihood estimation of subunits by soft-clustering spike-triggered stimuli, and demonstrate its effectiveness in visual neurons. Subunits estimated from parasol retinal ganglion cells (RGCs) in macaque retina partitioned the receptive field into compact regions, likely representing aggregated bipolar cell inputs. Joint clustering revealed shared subunits in neighboring RGCs, producing a parsimonious population model. Closed-loop validation, using stimuli lying in the null space of the linear receptive field, revealed stronger nonlinearities in OFF cells than ON cells. Responses to natural images, jittered to emulate fixational eye movements, were accurately predicted by the subunit model. Finally, the generality of the approach was demonstrated in macaque V1 neurons.</p>
Classical Center-Surround Receptive Fields Facilitate Novel Object Detection in Retinal Bipolar Cells
<p>Database and Figures in Igor format</p>
Data from: Neurons in primate visual cortex alternate between responses to multiple stimuli in their receptive field
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Data from: Visual receptive field properties of neurons in the mouse lateral geniculate nucleus
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Inference of nonlinear receptive field subunits with spike-triggered clustering
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Descriptive Study of Receptive Fields in Lower Limb Amputees
ClinicalTrials.gov study NCT03348605. IPD Sharing: NO. Countries: 1. Publications: 0.
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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.