Four neural circuit models and their role in the organization of voluntary movement are presented here. These circuits collectively control a ballistic type biped voluntary movement. The structure of each circuit, and its function is discussed. Three of the circuits are central and contribute to the construction of two classes of inputs, analogous to the alpha signals and gamma signals in biological systems. The fourth circuit plays a role in stabilization of the movement, and in compensation for the receptors. Digital computer simulations are undertaken to demonstrate the construction of all the intermediate signals and the response of a two link biped to these efferent signals.
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http://dx.doi.org/10.1007/BF00320387 | DOI Listing |
BMC Neurosci
January 2025
Department of Operative Dentistry and Periodontology, University Hospital Erlangen, Friedrich-Alexander University of Erlangen-Nürnberg, Erlangen, Germany.
Background: Parkinson's disease (PD) is a neurodegenerative disorder characterized by protein aggregates mostly consisting of misfolded alpha-synuclein (αSyn). Progressive degeneration of midbrain dopaminergic neurons (mDANs) and nigrostriatal projections results in severe motor symptoms. While the preferential loss of mDANs has not been fully understood yet, the cell type-specific vulnerability has been linked to a unique intracellular milieu, influenced by dopamine metabolism, high demand for mitochondrial activity, and increased level of oxidative stress (OS).
View Article and Find Full Text PDFBMC Public Health
January 2025
Centre for Sports, Health, and Civil Society, Research Unit for Active Living, Department of Sports Science and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark.
Background: Several studies have found that immigrants and descendants are less physically active than the majority population, particularly within sports clubs. However, most studies do not provide breakdowns by specific ethnic groups or organisational forms. Therefore, our paper analyses the influence of ethnicity, immigrant status, and sociodemographic and -economic characteristics on the physical activity participation of immigrants and descendants in sports clubs, commercial centres and self-organised activities in Denmark.
View Article and Find Full Text PDFBMC Geriatr
January 2025
Department of Health Economics, School of Public Health, Fudan University, 130 Dong'an Road, Shanghai, 200032, China.
Background: Multimorbidity has emerged as a significant challenge for healthcare systems globally. This study aims to examine the associations between key determinants of lifestyle behavior and various multimorbidity patterns.
Methods: In a cross-sectional sample of older adults (aged 60-79) from the Fifth National Physical Fitness Surveillance in Shanghai, latent class analysis (LCA) was used to identify multimorbidity patterns among 9 chronic diseases.
bioRxiv
January 2025
Centre for Vision Research, Centre for Integrative & Applied Neuroscience, Vision: Science to Applications Program, Connected Minds, Department of Biology, York University, Toronto, ON M3J 1P3, Canada.
Response preparation is accomplished by gradual accumulation in neural activity until a threshold is reached. In humans, such a preparatory signal, referred to as the lateralized readiness potential, can be observed in the EEG over sensorimotor cortical areas before execution of a voluntary movement. Although well-described for manual movements, less is known about preparatory EEG potentials for saccadic eye movements in humans and nonhuman primates.
View Article and Find Full Text PDFDigit Health
January 2025
Department of Obstetrics and Gynecology, Seoul National University College of Medicine, Jongno-gu, Seoul, Korea.
Objective: Accurate measurement of pelvic floor muscle (PFM) strength is crucial for the management of pelvic floor disorders. However, the current methods are invasive, uncomfortable, and lack standardization. This study aimed to introduce a novel noninvasive approach for precise PFM strength quantification by leveraging extracorporeal surface perineal pressure (ESPP) measurements and machine learning algorithms.
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