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http://dx.doi.org/10.1016/j.joto.2023.02.001 | DOI Listing |
Cognition
January 2025
Institute of Systems and Information Engineering, University of Tsukuba, Ibaraki 305-8573, Japan. Electronic address:
Pain perception is not solely determined by noxious stimuli, but also varies due to other factors, such as beliefs about pain and its uncertainty. A widely accepted theory posits that the brain integrates prediction of pain with noxious stimuli, to estimate pain intensity. This theory assumes that the estimated pain value is adjusted to minimize surprise, mathematically defined as errors between predictions and outcomes.
View Article and Find Full Text PDFInt J Mol Sci
November 2024
Department of Emergency Medicine, University of Nebraska Medical Center, Omaha, NE 68198, USA.
Type 2 diabetes mellitus (T2DM) is one of the most significant health issues worldwide, with associated healthcare costs estimated to surpass USD 1054 billion by 2045. The leading cause of death in T2DM patients is the development of cardiovascular disease (CVD). In the early stages of T2DM, patients develop cardiovascular autonomic dysfunction due to the withdrawal of cardiac parasympathetic activity.
View Article and Find Full Text PDFProc Biol Sci
November 2024
The Solomon H. Snyder Dept of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, Maryland 21205, USA.
Hearing mediates many behaviours critical for survival in echolocating bats, including foraging and navigation. Although most mammals are susceptible to progressive age-related hearing loss, the evolution of biosonar, which requires the ability to hear low-intensity echoes from outgoing sonar signals, may have selected against the development of hearing deficits in bats. Many echolocating bats exhibit exceptional longevity and rely on acoustic behaviours for survival to old age; however, relatively little is known about the ageing bat auditory system.
View Article and Find Full Text PDFIEEE Trans Neural Syst Rehabil Eng
October 2024
This study aimed to develop a novel framework to quickly personalize electromyography (EMG)-driven musculoskeletal models (MMs) as efferent neural interfaces for upper limb prostheses. Our framework adopts a generic upper-limb MM as a baseline and uses an artificial neural network-based policy to fine-tune the model parameters for MM personalization. The policy was trained by reinforcement learning (RL) to heuristically adjust the MM parameters to maximize the accuracy of estimated hand and wrist motions from EMG inputs.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!