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ShareScore release 0.9.0
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30 results for “recurrent neural network”
Research on AIS Recurrence Risk Prediction Model Using XGBoost Combined With Convolutional Neural Network Algorithm
ClinicalTrials.gov study NCT06796283. IPD Sharing: NO. Countries: 1. Publications: 1.
Double attention recurrent convolution neural network for answer selection
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Data from: Recurrent myocardial infarction: mechanisms of free-floating adaptation and autonomic derangement in networked cardiac neural control
The cardiac nervous system continuously controls cardiac function whether or not pathology is present. While myocardial infarction typically has a major and catastrophic impact, population studies have shown that longer-term risk for recurrent myocardial infarction and the related potential for sudden cardiac death depends mainly upon standard atherosclerotic variables and autonomic nervous system maladaptations. Investigative neurocardiology has demonstrated that autonomic control of cardiac function includes local circuit neurons for networked control within the peripheral nervous system. The structural and adaptive characteristics of such networked interactions define the dynamics and a new normal for cardiac control that results in the aftermath of recurrent myocardial infarction and/or unstable angina that may or may not precipitate autonomic derangement. These features are explored here via a mathematical model of cardiac regulation. A main observation is that the control environment during pathology is an extrapolation to a setting outside prior experience. Although global bounds guarantee stability, the resulting closed-loop dynamics exhibited while the network adapts during pathology are aptly described as 'free-floating' in order to emphasize their dependence upon details of the network structure. The totality of the results provide a mechanistic reasoning that validates the clinical practice of reducing sympathetic efferent neuronal tone while aggressively targeting autonomic derangement in the treatment of ischemic heart disease.
Data: Imitation learning of fish and swarm behavior with Recurrent Neural Networks
<p>Data for the Master's Thesis <em>Imitation learning of fish and swarm behavior with Recurrent Neural Networks</em></p>
Data from: Recurrent myocardial infarction: mechanisms of free-floating adaptation and autonomic derangement in networked cardiac neural control
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Audio samples from "A Physical Intelligent Instrument using Recurrent Neural Networks"
<p>These are the audio samples from chapter 3 and 4 of my thesis "A Physical Intelligent Instrument using Recurrent Neural Networks".</p>
Visualization Engineering Platform for Pulse Diagnosis of Traditional Chinese Medicine-The Research of Similar Moiré Feature Analyzing Approach Based on Recurrent Neural Network to Process the Measure
ClinicalTrials.gov study NCT04661605. IPD Sharing: Not stated. Countries: 1. Publications: 0.
An Adaptive Recurrent Neural Network for Remaining Useful Life Prediction of Lithium-ion Batteries
Prognostics is an emerging science of predicting the health condition of a system (or its components) based upon current and previous system states. A reliable predictor is very useful to a wide array of industries to predict the future states of the system such that the maintenance service could be scheduled in advance when needed. In this paper, an adaptive recurrent neural network (ARNN) is proposed for system dynamic state forecasting. The developed ARNN is constructed based on the adaptive/recurrent neural network architecture and the network weights are adaptively optimized using the recursive Levenberg-Marquardt (RLM) method. The effectiveness of the proposed ARNN is demonstrated via an application in remaining useful life prediction of lithium-ion batteries.*
Data and codebase for "Why do recurrent neural networks suddenly learn? Bifurcation mechanisms in neuro-inspired short-term memory tasks"
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Supplementary Materials for Sensuator: A Hybrid Sensor-Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks
<p>Videos accompanying the paper Sensuator: A Hybrid Sensor-Actuator Approach to Soft Robotic Proprioception Using Recurrent Neural Networks</p>
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.