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News
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.
August 24, 2026
Researchers Develop Method for Direct Generation of Regulatory DNA
Researchers at HSE University have developed a model for generating promoters and enhancers—DNA sequences that regulate gene activity. The model works directly with DNA nucleotides, without first transforming them into a continuous numerical representation. This solution could be useful for applications in synthetic biology and gene therapy. The study results were presented at the ICLR 2026 Workshop ‘Generative AI in Genomics (Gen^2): Barriers and Frontiers.’
August 21, 2026
Social Integration: At the Crossroads of Knowledge and Values
The International Laboratory for Social Integration Research (ILSIR) at HSE University studies the challenges faced by vulnerable groups and explores ways to help them participate fully in everyday life. To develop effective solutions, the laboratory’s researchers combine cutting-edge methods with practical fieldwork. In this interview with the HSE News Service, Laboratory Head Elena Iarskaia-Smirnova discusses the laboratory’s work.

 

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Default mode network activity is related to efficiency in a combined motion error and gambling task

Scientific Reports. 2026.
Chernov G., Mel’nikov M., Belianin A. V., Rudych P., Bezmaternykh D., Shakhzadayev R.

Decision-making under risk lies at the core of strategic action in human activities. Nevertheless, the neural mechanisms underlying decision-making in trial-and-error-based learning in the absence of explicit risk ratios have not been thoroughly understood. This study investigates the neural correlates of long-term success during repeated risky decision-making. We combined a motion detection task with a higher-level risk task in a functional magnetic resonance imaging (fMRI) paradigm. Twenty-five healthy adults participated in an fMRI task that required stopping a ball covertly moving at a constant speed at a certain spot. Participants initially selected a task difficulty level progressively related to the magnitude of the monetary reward and then attempted the task of chosen difficulty. Positive versus negative feedback (a monetary gain versus no gain) contrast comprised clusters in the striatum, executive control regions, and the Default mode network (DMN). The activity in brain regions associated with metacognitive functions (DMN nodes) and not in those involved in reward evaluation and risk-taking (striatum) was positively related to the exploration index, which reflects the effectiveness of risk-taking. This study provides insights into the functional roles of the DMN (especially, its anterior node) in high-level decision-making in risky environments, highlighting its importance in metacognitive control during reward-related learning.

Research target: Economics and Management
Language: English
DOI
Keywords: Decision-making
Publication based on the results of:
Multidisciplinary study of behavior and decision-making in health population and patients using behavioral, economic, neurocognitive, neuroeconomic, neurocomputational and neural network approaches (2025)
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