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August 25, 2026
Scientists Develop Algorithm for More Reliable Processors in Data Centres
Researchers from HSE MIEM and Samara University have developed the LRF-3D algorithm to automatically bypass idle nodes in three-dimensional networks-on-chip. Thanks to its hierarchical architecture, the algorithm outperforms existing solutions in both speed and path accuracy, improving processor reliability for use in data centres, supercomputers, and AI computing. The source code and test results are publicly available.
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Experience-dependent neuroplasticity in the hippocampus of bilingual young adults

eNeuro. 2025. Vol. 12. No. 6. Article ENEURO.0128-25.2025.
Gallo F., Voits T., Rothman J., Jubin Abutalebi, Shtyrov Y., Myachykov A.

Models of experience-dependent neuroplasticity predict that the acquisition and extensive use of a new skill trigger a nonlinear trajectory of neurostructural modifications, where initial expansion of relevant brain areas subsequently (once the skill is acquired) gives way to volumetric renormalization. Such predictions also apply in the domain of language during learning and/or simultaneous management of two (or more) linguistic systems. In a sample of 69 young adult Russian–English bilinguals, we tested the hypothesis that individual differences in bilingual engagement nonlinearly correlate with normalized volume of the hippocampus—a key learning-related brain region particularly amenable to experience-dependent plasticity. Results revealed an inverted U-shape association between second language engagement and left hippocampal gray matter volume. The present results replicate and expand the findings from aging populations, showing a nonlinear pattern of structural hippocampal plasticity in healthy young adults. These findings support the role of bilingualism as a promoter of experience-dependent neuroplasticity.

Research target: Medical and Health Sciences Psychology
Language: English
DOI
Text on another site
Keywords: bilingualismhippocampusMRIexperience-dependent plasticity
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