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| http://dx.doi.org/10.1007/s00134-013-3095-5 | DOI Listing |
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Weighted Echo State Graph Neural Networks Based on Robust and Epitaxial Film Memristors.
Adv Sci (Weinh)
January 2025
College of Physics Science & Technology, School of Life Sciences, Institute of Life Science and Green Development, Key Laboratory of Brain-Like Neuromorphic Devices and Systems of Hebei Province, Hebei University, Baoding, 071002, China.
Hardware system customized toward the demands of graph neural network learning would promote efficiency and strong temporal processing for graph-structured data. However, most amorphous/polycrystalline oxides-based memristors commonly have unstable conductance regulation due to random growth of conductive filaments. And graph neural networks based on robust and epitaxial film memristors can especially improve energy efficiency due to their high endurance and ultra-low power consumption.
View Article and Find Full Text PDFCognitive load detection through EEG lead wise feature optimization and ensemble classification.
Sci Rep
January 2025
Department of ECE, Kallam Haranadhareddy Institute of Technology, Guntur, Andhra Pradesh, India.
Cognitive load stimulates neural activity, essential for understanding the brain's response to stress-inducing stimuli or mental strain. This study examines the feasibility of evaluating cognitive load by extracting, selection, and classifying features from electroencephalogram (EEG) signals. We employed robust local mean decomposition (R-LMD) to decompose EEG data from each channel, recorded over a four-second period, into five modes.
View Article and Find Full Text PDFHumans rationally balance detailed and temporally abstract world models.
Commun Psychol
January 2025
Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.
How do people model the world's dynamics to guide mental simulation and evaluate choices? One prominent approach, the Successor Representation (SR), takes advantage of temporal abstraction of future states: by aggregating trajectory predictions over multiple timesteps, the brain can avoid the costs of iterative, multi-step mental simulation. Human behavior broadly shows signatures of such temporal abstraction, but finer-grained characterization of individuals' strategies and their dynamic adjustment remains an open question. We developed a task to measure SR usage during dynamic, trial-by-trial learning.
View Article and Find Full Text PDFDOME: Directional medical embedding vectors from electronic health records.
J Biomed Inform
January 2025
Harvard Medical School, Boston, MA, USA; VA Boston Healthcare System, Boston, MA, USA; Harvard T.H. Chan School of Public Health, Boston, MA, USA. Electronic address:
Motivation: The increasing availability of electronic health record (EHR) systems has created enormous potential for translational research. Recent developments in representation learning techniques have led to effective large-scale representations of EHR concepts along with knowledge graphs that empower downstream EHR studies. However, most existing methods require training with patient-level data, limiting their abilities to expand the training with multi-institutional EHR data.
View Article and Find Full Text PDFTwo-dimensional identification of lower limb gait features based on the variational modal decomposition of sEMG signal and convolutional neural network.
Gait Posture
December 2024
Engineering Research Center of the Ministry of Education for Intelligent Rehabilitation Equipment and Detection Technologies, Hebei University of Technology, Tianjin 300401, PR China; Hebei Key Laboratory of Robot Sensing and Human-robot Interaction, Hebei University of Technology, Tianjin 300401, PR China; School of Mechanical Engineering, Hebei University of Technology, Tianjin 300401, PR China. Electronic address:
Background: Gait feature recognition is crucial to improve the efficiency and coordination of exoskeleton assistance. The recognition methods based on surface electromyographic (sEMG) signals are popular. However, the recognition accuracy of these methods is poor due to ignoring the correlation of the time series of sEMG signals.
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