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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Quantization of Map-Based Neuronal Model for Embedded Simulations of Neurobiological Networks in Real-Time

American Journal of Engineering and Applied Sciences. 2016. Vol. 9. No. 4. P. 973–984.
Rulkov N., Hunt A. M., Rulkov P., Maksimov A. G.

The discreet-time (map-based) approach to modeling nonlinear dynamics of spiking and spiking-bursting activity of neurons has demonstrated its very high efficiency in simulations of neuro-biologically realistic behavior both in large-scale network models for brain activity studies and in real-time operation of Central Pattern Generator network models for biomimetic robotics. This paper studies the next step in improving the model computational efficiency that includes quantization of model variables and makes the network models suitable for embedded solutions. We modify a map-based neuron model to enable simulations using only integer arithmetic and demonstrate a significant reduction of computation time in an embedded system using readily available, inexpensive ARM Cortex L4 microprocessors.

 

Research target: Computer Science Physics Mathematics
Priority areas: IT and mathematics mathematics
Language: English
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Keywords: нейронные сетиquantisationMap-Based Neuron ModelsSpiking-Bursting ActivityEmbedded SolutionsBiomimetic Roboticsмодель нейрона в виде отображениядискретизациядинамика спайков и берстовбиомиметическая робототехника
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