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256
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ShareScore release 0.9.0
Dataset results
256 results for “computational modelling”
Data from: Computing the local field potential (LFP) from integrate-and-fire network models
Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.
Computational dataset, scripts and models for 'Lipid unsaturation promotes BAX and BAK pore activity during apoptosis'
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FIGURE 2 in Computational fluid dynamics modeling of fossil ammonoid shells
FIGURE 2. All of the shells employed in this study plotted in Westermann morphospace. The 3D models used for each member are shown to the right of the name/designation in the legend. Cardioceras, Oppelia, and Sphenodiscus are based on the shells employed in Jacobs (1992), Nautilus pompelius was created using laser scans and scaled to two different sizes (life-size [approx. 14.5 cm] and 5 cm diameter), and all other shells were created to match basic Westermann morphotypes (Westermann, 1996; Ritterbush and Bottjer, 2012).
Code and data for: A computational model for driver's cognitive state, visual perception and intermittent attention in a distracted car following task
<p>A source code and data dump for analyses of the article "A computational model for driver’s cognitive state, visual perception and intermittent attention in a distracted car following task"</p> <p>Code is under GNU AGPL-v3. Data under CC-BY-4.0</p> <p>Versioned code is available at https://gitlab.com/mulsimco/follow17 and https://gitlab.com/mulsimco/cfmodels</p> <p>See README.md in follow17 for usage.</p>
Structural and Functional Insight into EGFR and EGFRvIII using Computational Modeling and Docking
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Data from: Computational and experimental characterization of dVHL establish a Drosophila model of VHL syndrome
The von Hippel-Lindau (VHL) cancer syndrome is associated with mutations in the VHL gene. The pVHL protein is involved in response to changes in oxygen availability as part of an E3-ligase that targets the Hypoxia-Inducible Factor for degradation. pVHL has a molten globule configuration with marginal thermodynamic stability. The cancer-associated mutations further destabilize it. The Drosophila homolog, dVHL, has relatively low sequence similarity to pVHL, and is also involved in regulating HIF1-α. Using in silico, in vitro and in vivo approaches we demonstrate high similarity between the structure and function of dVHL and pVHL. These proteins have a similar fold, secondary and tertiary structures, as well as thermodynamic stability. Key functional residues in dVHL are evolutionary conserved. This structural homology underlies functional similarity of both proteins, evident by their ability to bind their reciprocal partner proteins, and by the observation that transgenic pVHL can fully maintain normal dVHL-HIF1-α downstream pathways in flies. This novel transgenic Drosophila model is thus useful for studying the VHL syndrome, and for testing drug candidates to treat it.
Data from: A probabilistic metric for the validation of computational models
A new validation metric is proposed that combines the use of a threshold based on the uncertainty in the measurement data with a normalised relative error, and that is robust in the presence of large variations in the data. The outcome from the metric is the probability that a model's predictions are representative of the real world based on the specific conditions and confidence level pertaining to the experiment from which the measurements were acquired. Relative error metrics are traditionally designed for use with series of data values but orthogonal decomposition has been employed to reduce the dimensionality of data matrices to feature vectors so that the metric can be applied to fields of data. Three previously published case studies are employed to demonstrate the efficacy of this quantitative approach to the validation process in the discipline of structural analysis, for which historical data was available; however, the concept could be applied to a wide range of disciplines and sectors where modelling and simulation plays a pivotal role.
Computational modeling of human multisensory spatial representation by a neural architecture
<p>Architecture and processed dataset used to train it, referring to the manuscript:</p> <p>Computational modeling of human multisensory spatial representation by a neural architecture</p>
Dual contribution of ASIC1a channels in the spinal processing of pain information by deep projection neurons revealed by computational modeling
<p>Experimental data used in the figures of the manuscript</p>
P in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
P. Ʋirescens
P in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
P. Ʋaucheri
P in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
P. tunesiacus
P in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
P. lusitanicus
P in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
P. liolepis
P in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
P. Ʋirescens
P in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
P. Ʋaucheri
P in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
P. liolepis
P in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
P. hispanicus
P in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
P. hispanicus
P in Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study
P. guadarramae
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.