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306 results for “mechanical modeling”
Data from: How biological attention mechanisms improve task performance in a large-scale visual system model
How does attentional modulation of neural activity enhance performance? Here we use a deep convolutional neural network as a large-scale model of the visual system to address this question. We model the feature similarity gain model of attention, in which attentional modulation is applied according to neural stimulus tuning. Using a variety of visual tasks, we show that neural modulations of the kind and magnitude observed experimentally lead to performance changes of the kind and magnitude observed experimentally. We find that, at earlier layers, attention applied according to tuning does not successfully propagate through the network, and has a weaker impact on performance than attention applied according to values computed for optimally modulating higher areas. This raises the question of whether biological attention might be applied at least in part to optimize function rather than strictly according to tuning. We suggest a simple experiment to distinguish these alternatives.
Trained models for "Neural mechanisms of working memory accuracy revealed by recurrent neural networks"
<p>There are three trained models:</p> <p>1. data/6tasks_8loc_256neuron_odr3000_seed0: odr(3s delay ) task with 8 input units in a ring and 256 neurons</p> <p>2. data/6tasks_360loc_256neuron: odr(1.5s delay) task with 360 input units in a ring and 256 neurons</p> <p>3. odr_mix_uniform_00_30_01step_6tasks: odr(variable delay from 0 to 3s with 0.1s step) task with 8 input units in a ring and 256 neurons</p>
Figure 7 in A dynamic model for the evolution of sabrecat predatory bite mechanics
Figure 7. The relative force output at the upper canine [(I(Tf(cos Q))/Oca) where I, inlever moment arm; Tf, theoretical force output from the muscle fibre; Q, angle between the effective (rotational) torque about the temporomandibular joint and T; O, outlever moment arm to the centre of C1] at gape angles from occlusion to maximal inferred gape in: A, deep f ca masseter + zygomaticomandibularis fibre 1; B, deep masseter + zygomaticomandibularis fibre 2; C, deep masseter + zygomaticomandibularis fibre 3; D, deep masseter + zygomaticomandibularis fibre 4; E, deep masseter + zygomaticomandibularis fibre 5; F, superficial masseter. Negative values imply that an adductor has shifted to becoming an abductor at this gape angle.
Figure 3 in A dynamic model for the evolution of sabrecat predatory bite mechanics
Figure 3. The angle (Q) between the effective (rotational) torque about the temporomandibular joint (Te) and the theoretical force output from the muscle fibre (Tf) at gape angles from occlusion to maximal inferred gape in: A, deep masseter + zygomaticomandibularis fibre 1; B, deep masseter + zygomaticomandibularis fibre 2; C, deep masseter + zygomaticomandibularis fibre 3; D, deep masseter + zygomaticomandibularis fibre 4; E, deep masseter + zygomaticomandibularis fibre 5; F, superficial masseter.
Figure 2 in A dynamic model for the evolution of sabrecat predatory bite mechanics
Figure 2. The angle (Q) between the effective (rotational) torque about the temporomandibular joint (Te) and the theoretical force output from the muscle fibre (Tf) at gape angles from occlusion to maximal inferred gape in: A, temporalis fibre 1; B, temporalis fibre 3; C, temporalis fibre 5; D, temporalis fibre 7; E, temporalis fibre 8; F, temporalis fibre 10.
Figure 5 in A dynamic model for the evolution of sabrecat predatory bite mechanics
Figure 5. The relative ability of individual muscle fibres to generate rotational torque about the temporomandibular joint (TMJ) [effective (rotational) torque about the TMJ divided by the theoretical force output from the muscle fibre (Te/Tf)] at gape angles from occlusion to maximal inferred gape in: A, deep masseter + zygomaticomandibularis fibre 1; B, deep masseter + zygomaticomandibularis fibre 2; C, deep masseter + zygomaticomandibularis fibre 3; D, deep masseter + zygomaticomandibularis fibre 4; E, deep masseter + zygomaticomandibularis fibre 5; F, superficial masseter.
Figure 4 in A dynamic model for the evolution of sabrecat predatory bite mechanics
Figure 4. The relative ability of individual muscle fibres to generate rotational torque about the temporomandibular joint (TMJ) [effective (rotational) torque about the TMJ divided by the theoretical force output from the muscle fibre (Te/Tf)] at gape angles from occlusion to maximal inferred gape in: A, temporalis fibre 1; B, temporalis fibre 3; C, temporalis fibre 5; D, temporalis fibre 7; E, temporalis fibre 8; F, temporalis fibre 10.
Figure 6 in A dynamic model for the evolution of sabrecat predatory bite mechanics
Figure 6. The relative force output at the upper canine [(I(Tf(cos Q))/Oca) where I, inlever moment arm; Tf, theoretical force output from the muscle fibre; Q, angle between the effective (rotational) torque about the temporomandibular joint and T; O, outlever moment arm to the centre of C1] at gape angles from occlusion to maximal inferred gape in: A, f ca temporalis fibre 1; B, temporalis fibre 3; C, temporalis fibre 5; D, temporalis fibre 7; E, temporalis fibre 8; F, temporalis fibre 10. Negative values imply that an adductor has shifted to becoming an abductor at this gape angle.
Figure 1 in A dynamic model for the evolution of sabrecat predatory bite mechanics
Figure 1. The ability of the mandibular adductors to generate torque about the temporomandibular joint (TMJ) was estimated at ten regularly spaced intervals of the M. temporalis (T1–T10); at five regularly spaced intervals of the M. masseter profunda + M. zygomaticomandibularis (M1–M5); and the anterior-most insertion of the M. masseter superficialis. A, lion (Panthera leo; CN3503; ♂), with mandible at occlusion and at estimated maximal gape, illustrating torque about the TMJ at T1 (green vectors); B, Smilodon fatalis [LACMHC2001-173 (cranium) and LACMHC2001-4543 (mandible)] with mandible at occlusion and at estimated maximal gape, illustrating torque about the TMJ at M2 (blue vectors), and at SM (red vectors). Abbreviations: Im, inlever moment arm for masseter muscle fibre torque about the TMJ; I, inlever moment arm for temporalis muscle fibre torque about the TMJ; O, outlever moment arm to the carnassial (P4) t c paracone apex; O, outlever moment arm to the centre of C1; T, effective (rotational) torque about the TMJ; T, theoretical ca e f force output from the muscle fibre; Q, angle between Te and Tf. Scale bars = 10 cm.
Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years
<p>Monte Carlo Simulations results for estimating an offshore structure fatigue life with a Fracture Mechanics based crack growth model, after additional information was considered through Bayesian inference at t=13 years</p>
Pore-scale modeling and investigation on thermal-hydro-mechanical-chemical coupled rock dissolution and fracturing process
<p>Attached files include the executable file of the pore-scale multi-field coupled LBM-DEM program written by C language, post-processing programs to record the reactive surface area and reactive temperature written by MATLAB, and the simulation results of rock acid fracturing with 20MPa at the injection hole.</p>
Hygro-thermo-mechanical model for concrete pavement from an early age to a long-term performance
<p>A new 3D hygro-thermo-mechanical model for a concrete pavement slab is formulated, capturing its behavior from casting to long-term drying. The model includes a microprestress-solidification viscoelastic model for concrete ageing creep. Calibrations and validations are based on lab experiments and a four year continuous monitoring of a real highway slab. The results show that slab drying leads to the highest principal tensile stress on the surface as high as 3.5 MPa. Simulated temperature cycles and traffic loading yield smaller stresses. In this regard, concrete crack resistance during drying should be considered in material and structural design.</p>
Model output publication 'Mechanisms of salt overspill at estuarine network junctions explained with an idealized model'
<p>The model output for the publication in JGR: Oceans titled 'Mechanisms of salt overspill at estuarine network junctions explained with an idealized model'</p>
Processed dataset used to train models in the study "Integrative modeling of lncRNA-chromatin interaction maps reveals diverse mechanisms of nuclear retention"
<p>The processed dataset used to train models in the study "Integrative modeling of lncRNA-chromatin interaction maps reveals diverse mechanisms of nuclear retention".</p>
Mechanism of building a machine learning model and its main features
<p>This figure is a flowchart summarizing the steps required to build a machine learning model in general way.</p>
Dataset for: Modeling Allosteric Mechanisms of Eukaryotic Type II Topoisomerases
<p>The set of molecular dynamics trajectories generated and analyzed in the paper "Modeling Allosteric Mechanisms of Eukaryotic Type II Topoisomerases." <strong>Citations should refer directly to the manuscript. </strong> </p> <p>The uploaded zip file "trajectory_data.zip" contains trajectories for the (1) ATP (2) ADP (3) Apo (4) D26N (5) R1128G and (6) D26N/R1128G h3k9ac systems. In each directory is 5 XTC files, which correspond to the trajectories for the 5 different simulations performed for each system, along with an AMBER-formated PRMTOP file. Solvent molecules were removed from each file, and trajectories were strided such that there is one frame per 100 ps. </p>
Fig. 3 in Modeling the peroxyl radical scavenging behavior of Carnosic acid: Mechanism, kinetics, and effects of physiological environments
Fig. 3. Deprotonated forms of CA under the physiological aqueous environment. The proton affinity (PA) values are in kcal/mol.
Fig. 6 in Modeling the peroxyl radical scavenging behavior of Carnosic acid: Mechanism, kinetics, and effects of physiological environments
Fig. 6. Optimized structures of the TSs of CA + HOO• reaction in physiological environments (W = water and PE = pentyl ethanoate). Distances are in Aand angles in ◦.
Fig. 2 in Modeling the peroxyl radical scavenging behavior of Carnosic acid: Mechanism, kinetics, and effects of physiological environments
Fig. 2. The stable conformation, HOMO, LUMO, and ESP of CA calculated at M06–2X/6–311++ G(d,p) level.
Fig. 4 in Modeling the peroxyl radical scavenging behavior of Carnosic acid: Mechanism, kinetics, and effects of physiological environments
Fig. 4. Computed BDE values in kcal/mol of CA in the gas phase (G) and pentyl ethanoate (PE) as well as of CA– in water (W).
ScienceDex guides
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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.