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News
October 1, 2026
HSE Researchers Show How Congenital Motor Disorders Affect Brain Development
Researchers from HSE University’s Institute for Cognitive Neuroscience have synthesised the findings of their previous studies on brain development in children with obstetric brachial plexus palsy and arthrogryposis. Their analysis shows that impaired motor function in early childhood not only limits children’s motor experience but also affects memory, categorical thinking, and information processing. The study has been published in Frontiers in Psychology.
October 1, 2026
Window into the Body: Scientists Develop Neural Network to Detect Risk of 15 Diseases from Retinal Images
Russian universities, with the participation of HSE University, Sber, and Z-union, have developed a neural network that can simultaneously assess the risk of 15 types of pathology from retinal photographs, including not only eye diseases but also cardiovascular conditions. The AI system can help clinicians detect potentially concerning changes at an early stage, identify signs reflecting the condition of retinal blood vessels, and determine whether a patient may need further examination. The paper has been published in Frontiers in Medicine.
September 30, 2026
'We Did Not Limit the Time for Questions'
The International Laboratory for Supercomputer Atomistic Modelling and Multi-Scale Analysis at HSE University held a major conference on molecular dynamics. Participants had the opportunity to attend all the presentations, while speakers were given as much time as they needed to answer questions. The HSE News Service interviewed Grigory Smirnov, Head of the Laboratory, and Genri Norman, Chief Research Fellow, about the conference preparations and the discussions it generated.

 

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Towards Automatic Manipulation of Arbitrary Structures in Connectivist Paradigm with Tensor Product Variable Binding

P. 375–383.
Demidovskij A.

Building a bridge between symbolic and connectionist level of computations requires constructing a full pipeline that accepts symbolic structures as an input, translates them to distributed representation, performs manipulations with this representation equivalent to symbolic manipulations and translates it back to the symbolic structure. This work proposes neural architecture that is capable of joining two structures which is an essential part of structure manipulation step in the connectionist pipeline. Verification of the architecture demonstrates scalability of the solution, a set of advice for engineering practitioners was elaborated.

Language: English
Full text
DOI
Keywords: artificial neural networksUnsupervised learningtensor computationsConnectionism

In book

Advances in Neural Computation, Machine Learning, and Cognitive Research III
Springer, 2020.
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