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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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Ultra-rapid and automatic interplay between L1 and L2 semantics in late bilinguals: EEG evidence

Cortex. 2022. Vol. 151. P. 147–161.
Bermúdez-Margaretto B., Gallo F., Novitskiy N., Myachykov A., Petrova A., Shtyrov Y.

Converging behavioral and neuroimaging evidence suggests parallel activation of native (L1) and second (L2) language codes in bilinguals, with the modulation of the N400 as the most likely neural correlate of such L1-L2 interplay at lexico-semantic level. However, this relatively late effect may reflect secondary controlled processes, in contrast to earlier modulations found in monolinguals (<200 msec) indicative of fast and automatic lexico-semantic L1 access, which has so far not been documented for bilingualism. To address this, we investigated early neurophysiological crosslinguistic activation during bilingual word access. EEG signals were recorded from a group of late bilinguals during a masked-priming crosslinguistic task in which L1 (Russian) words were presented as subliminal primes for 50 msec before L2 (English) target words. Prime-target pairs matched either phonologically only, semantically only, both phonologically and semantically, or did not match. Cluster-based random permutation analyses revealed a main effect of semantic similarity at 40–60 msec over centro-posterior scalp sites, reflecting a negative-going shift of ERP amplitudes for semantically similar prime-target pairs. Importantly, neural source reconstruction showed activations within a left-hemispheric network comprising the middle and superior temporal cortex and the angular gyrus as the most likely neural substrate of this early semantic effect. Furthermore, analyses also revealed significant differences over frontocentral sites for the main effect of semantic and phonological similarity, ranging from 312 to 356 and 380–444 msec respectively, thus confirming previously described N400 crosslinguistic effects. Our findings confirm the existence of an integrated brain network for the bilingual lexicon and reveal the earliest (∼50 msec) crosslinguistic effect reported so far, suggesting fast and automatic L1–L2 interplay, followed by later (possibly top-down controlled) processing stages.

Research target: Psychology
Language: English
DOI
Text on another site
Keywords: semanticswordbilingualismEEGlexical accessERPP50 (P1), N400, N350Cluster based permutationCrosslinguistic interaction
Publication based on the results of:
Multimodal study of neural mechanisms of cognition and decision making in normal and pathological brain (2022)
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