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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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Patterns of Cortical Activity in a Silent Single-Word Reading Task Depend on Word Frequency and Age-Related Differences: An MEG Study

Psychophysiology. 2025. Vol. 62. No. 11. Article e70192.
Samoylov I., Bolgina T., Lonshakov G., Gomozova M., Arutiunian V., Dragoy O.

This study investigates how word frequency and age modulate the amplitude, temporal, and spatial patterns of cortical activation during silent single-word reading, as measured by magnetoencephalography (MEG). We recorded MEG data from 30 neurotypical adults and 30 typically developing children during a silent reading task involving high-frequency words, low-frequency words, and pseudowords, with cortical activation analyzed using event-related
fields (ERF) and peak latency (PL). In both adults and children, high-frequency words elicited lower ERF amplitudes and faster processing times compared to low-frequency words and pseudowords. While similar neural regions were activated across stimulus types, children demonstrated significantly higher amplitudes and longer processing times than adults. These results indicate that word frequency significantly modulates the neural dynamics of reading, with high-frequency words processed more efficiently. Furthermore, the data suggest that the reading pathways in children are still maturing, as evidenced by their increased neural activation and delayed processing. This developmental 
difference, particularly the demonstration of frequency-dependent processing in the superior temporal gyrus of children, offers unique evidence for the maturation of distinct lexical and sublexical reading pathways, consistent with the dual-route cascaded theory.

Research target: Humanities Psychology
Language: English
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Keywords: word frequencymagnetoencephalographyreading developmentdual‐route cascaded (DRC) modelsingle word reading
Publication based on the results of:
Neural foundations of language and its impairments: From basic research to clinical practice (2025)
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