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1,007 results for “Smoothing”
Cortical mechanisms of smooth eye movements revealed by dynamic covariations of neural and behavioral responses
<p>Neural activity in the frontal eye fields controls smooth pursuit eye movements, but the relationship between single neuron responses, cortical population responses, and eye movements is not well understood. We describe an approach to dynamically link trial-to-trial fluctuations in neural responses to parallel variations in pursuit and demonstrate that individual neurons predict eye velocity fluctuations at particular moments during the course of behavior, while the population of neurons collectively tiles the entire duration of the movement. The analysis also reveals the strength of correlations in the eye movement predictions derived from pairs of simultaneously recorded neurons and suggests a simple model of cortical processing. These findings constrain the primate cortical code for movement, suggesting that either a few neurons are sufficient to drive pursuit at any given time or that many neurons operate collectively at each moment with remarkably little variation added to motor command signals downstream from the cortex.</p>
Saccades Exert Spatial Control of Motion Processing for Smooth Pursuit Eye Movements
<p>Saccades modulate the relationship between visual motion and smooth eye movement. Before a saccade, pursuit eye movements reflect a vector average of motion across the visual field. After a saccade, pursuit primarily reflects the motion of the target closest to the endpoint of the saccade. We tested the hypothesis that the saccade produces a spatial weighting of motion around the endpoint of the saccade. Using a moving pursuit stimulus that stepped to a new spatial location just before a targeting saccade, we controlled the distance between the endpoint of the saccade and the position of the moving target. We demonstrate that the smooth eye velocity following the targeting saccade weights the presaccadic visual motion inputs by the distance from their location in space to the endpoint of the saccade, defining the extent of a spatiotemporal filter for driving the eyes. The center of the filter is located at the endpoint of the saccade in space, not at the position of the fovea. The filter is stable in the face of a distracter target, is present for saccades to stationary and moving targets, and affects both the speed and direction of the postsaccadic eye movement. The spatial filter can explain the target-selecting gain change in postsaccadic pursuit, and has intriguing parallels to the process by which perceptual decisions about a restricted region of space are enhanced by attention. The effect of the spatial saccade plan on the pursuit response to a given retinal motion describes the dynamics of a coordinate transformation.</p>
Fig. 10 in Description of head scalation variation, hemipenis, reproduction, and behavior of the Indian Smooth Snake, Coronella brachyura (Günther 1866)
Fig. 10. Graph showing distribution of Coronella brachyura. Prepared by Dikansh S. Parmar.
Fig. 6 in Description of head scalation variation, hemipenis, reproduction, and behavior of the Indian Smooth Snake, Coronella brachyura (Günther 1866)
Fig. 6. Coronella brachyura preying upon gecko, coiling around it. Photo credit Dikansh S. Parmar.
Fig. 7. Captive individual did not eat frogs when offered. Photo credit Dikansh S in Description of head scalation variation, hemipenis, reproduction, and behavior of the Indian Smooth Snake, Coronella brachyura (Günther 1866)
Fig. 7. Captive individual did not eat frogs when offered. Photo credit Dikansh S. Parmar.
Fig. 8 in Description of head scalation variation, hemipenis, reproduction, and behavior of the Indian Smooth Snake, Coronella brachyura (Günther 1866)
Fig. 8. Eggs of Coronella brachyura in hypoosmotic condition. Photo credit Vedant Lala.
Fig. 3. BNHS 794 in Short Communication On the distribution, taxonomy, and natural history of the Indian Smooth Snake, Coronella brachyura (Günther, 1866)
Fig. 3. BNHS 794, collected by Abdulali (1935) from Mumbai, India.
Fig. 1 in Short Communication On the distribution, taxonomy, and natural history of the Indian Smooth Snake, Coronella brachyura (Günther, 1866)
Fig. 1. Dorsal aspect of Coronella brachyura in life, from Surat, Gujarat, India.
Fig. 1 in Diet of the smooth-coated otter Lutrogale perspicillata (Geoffroy, 1826) at natural and modified sites in Singapore
Fig. 1. Location of the four study sites along the northern shore of Singapore.
Figure 4 in Vocal repertoire and group-specific signature in the Smooth-billed Ani, Crotophaga ani Linnaeus, 1758 (Cuculiformes, Aves)
Figure 4. Boxplots (median and quartiles) of acoustic parameters of the similar vocalizations of Charqueada and Guararema groups of Smooth-billed Ani. Vocalizations:"Ahnee","Whine", "Pre-flight", "Flight" and "Vigil". Acoustic parameters: DUR = duration; MPF = maximum peak frequency; MFF = maximum fundamental frequency; MIF = minimum frequency; MAF = maximum frequency.
Figure 3 in Vocal repertoire and group-specific signature in the Smooth-billed Ani, Crotophaga ani Linnaeus, 1758 (Cuculiformes, Aves)
Figure 3. Spectrograms of the ten types of vocalizations of the Smooth-billed Ani: "Ahnee" (A), "Whine" (B, C, D, E, F and G), "Pre-flight" (H), "Shout" (I), "Flight" (J and K), "Hoot" (L), "Grunt" (M), "Ee-oo-ee" (N), "Vigil" (O),"INR" (P and Q).
Figure 1 in Vocal repertoire and group-specific signature in the Smooth-billed Ani, Crotophaga ani Linnaeus, 1758 (Cuculiformes, Aves)
Figure 1. Location of the studied groups of Smooth-billed Ani in the municipality of Alegre, ES, Brazil.
Vehkaoja etal Infrastructure influences urban smooth newts
<p>Dataset contains information on smooth newts in metropolitan Helsinki, Finland. The file also contains land use information obtained through GIS analysis.</p>
A Deep Learning Tool for the Assessment of Pavement Smoothness and Aggregate Segregation during Construction
<p>Pavement construction monitoring and quality assurance (QA) practices are mostly based on costly, discrete, and destructive methods. Most quality assurance programs are based on pavement construction procedures encompassing in-situ coring for layer thickness determination, density measurements, laboratory testing to measure volumetric properties, and smoothness measurements in case of the availability of a profiler. The main objective of this study was to develop a machine learning-based classifier for predicting pavement roughness and aggregate segregation based on digital image analysis, image recognition, and deep learning machine models. The developed Convolution Neural Networks (CNN) models were trained, tested, and validated using 600-pavement surface images extracted from the Louisiana Department of Transportation and Development (LaDOTD) Pavement Management System (PMS) and 129 pavement images collected from three construction sites a few days after paving. These images were randomly divided into 70%, 15%, and 15% for the training, testing, and validation phases, respectively. The roughness model achieved 93.8% and 92.6% accuracy in the training and validation stages; respectively, and predicted the International Roughness Index (IRI) values with a coefficient of determination R<sup>2</sup> of 0.98 and a Root-Mean Square Error (RMSE) of 3.5%. In addition, the developed image-processing model for the detection of aggregate segregation achieved adequate accuracy. Furthermore, the developed segregation detection procedure adequately described the relationship between mix density and segregation.</p>
Parameters for smooth exponential atmosphere density model based on Jacchia-77
<p>The data sets provided here can be used to derive the <a href="https://doi.org/10.1016/j.asr.2019.03.016">smooth exponential atmosphere density</a> profile, fitted to the Jacchia-77 atmosphere density model. Both the static (at <span class="math-tex">\(T_\infty = 750, 1000, 1250K\)</span>) and variable model (for <span class="math-tex">\(T_\infty \in [650, 1350] K\)</span>) parameters are available.</p> <p>The model was derived to increase the accuracy of semi-analytically propagated orbits, in particular highly eccentric ones.</p>
Airway Smooth Muscle and Asthma Severity
ClinicalTrials.gov study NCT00779870. IPD Sharing: NO. Countries: 1. Publications: 1.
Influence of Mucosa Tissue Thickness on Marginal Bone Loss of Implants With Smooth Collars
ClinicalTrials.gov study NCT02925078. IPD Sharing: NO. Countries: 1. Publications: 12.
Acute Airway Vascular Smooth Muscle Effects of Inhaled Budesonide
ClinicalTrials.gov study NCT01219738. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Dietary adaptations along the Northern limit of distribution: What does the smooth snake (Coronella austriaca) eat in Norway? Metabarcoding of stomach content and visual analysis of faeces
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Data from: Are skyline plot-based demographic estimates overly dependent on smoothing prior assumptions?
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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.