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June 5, 2026
Neural Network Maps as a Method for Constructing Mathematical Models
Scientists from HSE University–Nizhny Novgorod and the Institute of Physics Belgrade, Serbia, are jointly exploring the application of machine learning techniques and neural networks to the study of nonlinear dynamics. Natalya Stankevich, Leading Research Fellow at the Laboratory of Topological Methods in Dynamics of the Faculty of Informatics, Mathematics, and Computer Science at HSE University–Nizhny Novgorod, spoke to the HSE News Service about this international project.
June 5, 2026
‘In the Age of Technology, It Is Interesting to Look into the Past and Think about What We Can Take from It
Polina Tabakova decided to apply for a Philology degree at HSE in Nizhny Novgorod because she grew up in Mari El and did not want to move far away from the Russian forests. In an interview for the Young Scientists of HSE University project, she spoke about the genre of the campus novel, the existential drama of Kolobok, and a blackout version of Eugene Onegin.
June 5, 2026
HSE Scientists Develop Method to Compress Large Language Models Without Losing Quality
Researchers from the AI and Digital Science Institute at the HSE Faculty of Computer Science have developed a new compression method for large language models such as GPT and LLaMA that reduces their size by 25–36% without additional training or significant loss of accuracy. This is the first approach to use mathematical transformations—specifically, rotations of model weights—to make models more amenable to compression with structured matrices. The study results have been published in ACL Findings 2025. The code is available on GitHub.

 

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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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