Memory and learning are studied in a model neural network made from component cells with a variety of realistic intrinsic dynamic behaviors. Modulation of intrinsic cellular characteristics causes a network to switch between two entirely different modes of operation. In one mode the network acts as a selective, long-term associative memory, whereas in the other it is a nonselective, short-term latching memory. Such functional modulation can be used as a mechanism for initiating and terminating learning in a network associative memory.
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http://dx.doi.org/10.1073/pnas.87.23.9241 | DOI Listing |
Sleep
January 2025
UR2NF-Neuropsychology and Functional Neuroimaging Research Unit affiliated at CRCN - Centre for Research in Cognition and Neurosciences and UNI - ULB Neuroscience Institute, Université Libre de Bruxelles (ULB), Brussels, Belgium.
Enhancing the retention of recent memory traces through sleep reactivation is possible via Targeted Memory Reactivation (TMR), involving cueing learned material during post-training sleep. Evidence indicates detectable short-term microstructural changes in the brain within an hour after motor sequence learning, and post-training sleep is believed to contribute to the consolidation of these motor memories, potentially leading to enduring microstructural changes. In this study, we explored how TMR during post-training sleep affects performance gains and delayed microstructural remodeling, using both standard Diffusion Tensor Imaging (DTI) and advanced Neurite Orientation Dispersion & Density Imaging (NODDI).
View Article and Find Full Text PDFEval Rev
January 2025
Global Development Network, Lanzhou University and Director of Evaluation, New Delhi, India.
Official development agencies are increasingly supporting civil society lobby and advocacy (L&A) to address poverty and human rights. However, there are challenges in evaluating L&A. As programme objectives are often to change policies or practices in a single institution like a Government Ministry, L&A programmes are often not amenable to large-n impact evaluation methods.
View Article and Find Full Text PDFMAGMA
January 2025
Aix Marseille Univ, CNRS, CRMBM, Marseille, France.
Objective: Segmentation of individual thigh muscles in MRI images is essential for monitoring neuromuscular diseases and quantifying relevant biomarkers such as fat fraction (FF). Deep learning approaches such as U-Net have demonstrated effectiveness in this field. However, the impact of reducing neural network complexity remains unexplored in the FF quantification in individual muscles.
View Article and Find Full Text PDFMed Biol Eng Comput
January 2025
Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.
Performing automatic and standardized 4D TEE segmentation and mitral valve analysis is challenging due to the limitations of echocardiography and the scarcity of manually annotated 4D images. This work proposes a semi-supervised training strategy using pseudo labelling for MV segmentation in 4D TEE; it employs a Teacher-Student framework to ensure reliable pseudo-label generation. 120 4D TEE recordings from 60 candidates for MV repair are used.
View Article and Find Full Text PDFWomens Health (Lond)
January 2025
College of Nursing, University of Utah, Salt Lake City, UT, USA.
Background: Postpartum is a critical period to interrupt weight gain across the lifespan, decrease weight-related risk in future pregnancies, promote healthy behaviors that are often adopted during pregnancy, and improve long-term health. Because the postpartum period is marked by unique challenges to a person's ability to prioritize healthy behaviors, a multi-level/domain approach to intervention beyond the individual-level factors of diet and activity is needed.
Objectives: The purpose of this study was to understand postpartum people's perceptions about the relationship between their social networks and support, and their health behaviors and weight.
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