Structural plasticity of dendritic spines underlies learning, memory and cognition in the cerebral cortex. We here summarize fifteen rules of spine structural plasticity, or 'spine learning rules.' Together, they suggest how the spontaneous generation, selection and strengthening (SGSS) of spines represents the physical basis for learning and memory. This SGSS mechanism is consistent with Hebb's learning rule but suggests new relations between synaptic plasticity and memory. We describe the cellular and molecular bases of the spine learning rules, such as the persistence of spine structures and the fundamental role of actin, which polymerizes to form a 'memory gel' required for the selection and strengthening of spine synapses. We also discuss the possible link between transcriptional and translational regulation of structural plasticity. The SGSS mechanism and spine learning rules elucidate the integral nature of synaptic plasticity in neuronal network operations within the actual brain tissue.
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http://dx.doi.org/10.1111/j.1460-9568.2010.07344.x | DOI Listing |
Health Sci Rep
January 2025
Research Center for Environmental Determinants of Health (RCEDH), Health Institute Kermanshah University of Medical Sciences Kermanshah Iran.
Background And Aims: Infertility, as defined by the World Health Organization, is the inability to conceive after 12 months of regular, unprotected intercourse. This study aimed to identify factors influencing infertility by applying data mining techniques, specifically rule-mining methods, to analyze diverse patient data and uncover relevant insights. This approach involves a thorough analysis of patients' clinical characteristics, dietary habits, and overall conditions to identify complex patterns and relationships that may contribute to infertility.
View Article and Find Full Text PDFNat Commun
January 2025
School of Future Technology, University of Chinese Academy of Sciences, 100190, Beijing, PR China.
In bioneuronal systems, the synergistic interaction between mechanosensitive piezo channels and neuronal synapses can convert and transmit pressure signals into complex temporal plastic pulses with excitatory and inhibitory features. However, existing artificial tactile neuromorphic systems struggle to replicate the elaborate temporal plasticity observed between excitatory and inhibitory features in biological systems, which is critical for the biomimetic processing and memorizing of tactile information. Here we demonstrate a mechano-gated iontronic piezomemristor with programmable temporal-tactile plasticity.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
School of Computer Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei, 230027, China.
Background: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.
View Article and Find Full Text PDFIISE Trans Occup Ergon Hum Factors
January 2025
The Bradley Department of Electrical and Computer Engineering, College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, VA, USA.
OCCUPATIONAL APPLICATIONSInnovative tools that align with modern learners' preferences are essential for training in safety-critical professions like Air Traffic Control/Management. This study evaluated a Virtual Reality Visual Flight Rules 3D Map Visualization Tool designed to meet the Federal Aviation Administration's (FAA) modernization goals. The tool immerses trainee in contextually accurate environments, enhancing engagement and self-paced learning.
View Article and Find Full Text PDFPolymers (Basel)
January 2025
Institute for Plastics Processing (IKV) in Industry and Craft, RWTH Aachen University, Seffenter Weg 201, 52074 Aachen, Germany.
The need for an efficient adaptation of existing polypropylene (PP) formulations or the creation of new formulations has become increasingly important in various industries. Variations in viscosity resulting from changes in raw materials, fillers, and additives can have a significant impact on the processing and quality of PP products. This study presents the development of an analytical model designed to predict the shear viscosity of complex PP blends.
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