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  • Возможности индивидуального фМРТ-картирования мозговых основ рабочей памяти с помощью задачи «N шагов назад»
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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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Возможности индивидуального фМРТ-картирования мозговых основ рабочей памяти с помощью задачи «N шагов назад»

Вестник рентгенологии и радиологии. 2025. Т. 106. № 6. С. 192–206.
Pechenkova E., Паникратова Я. Р., Королькова О. А., Пчелинцева М. Е., Смирнова А. В., Mening S., Маковская Л. А., Синицын В. Е.

Background. Individual activation maps based on brain functional magnetic resonance imaging (fMRI) data cannot yet be applied to refine a patient's diagnosis and prognosis. However, tracking activation dynamics in the same patient during the course of treatment or rehabilitation can be useful for assessing their efficacy. Population-based studies often fail to identify clinically significant activation parameters and to aid interpretation. To address this gap, we propose the examination of individual differences in activation in correspondence with the state of the mental process of interest.
 

Objective: to analyze individual differences in brain activation evoked by the n-back task (working memory updating) in young and elderly healthy participants related to accuracy and response times in this task. 

Material and methods. fMRI was recorded in 16 young (18–35 years) and 16 elderly (60–75 years) healthy right-handed participants while they performed the n-back task. Group-level activation was assessed in the 2-back versus 0-back conditions. The effects of age (young/elderly), material type (verbal/nonverbal), accuracy and response times were assessed.
 

Results. In all participants, regardless of age, less effective performance in the 2-back task was accompanied by more pronounced activation. Lower accuracy was coupled with higher activation in the visual cortex bilaterally and in the right inferior frontal gyrus, while a higher response time was associated with greater activation in the right frontal components of the frontoparietal network and the right frontal pole. 

Conclusion. Our findings explain a very small portion of the variance in activation patterns in the working memory tasks, so they cannot yet be used to interpret individual activation maps. However, they could pave the way for assessing dynamics of individual patterns over time if successfully replicated in a longitudinal study.

Research target: Psychology Biology
Language: Russian
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Keywords: пожилой возрастрабочая памятьindividual differencesиндивидуальные различияfunctional magnetic resonance imagingфункциональная магнитно-резонансная томографияelderly agen-back taskвербальный материалневербальный материал working memoryздоровое старениеverbal material nonverbal materialyoung agehealthy agingзадача «N шагов назад»молодой возраст
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