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Regular version of the site

Dean — Ivan Arzhantsev


First Deputy Dean — Tamara Voznesenskaya


Deputy Dean for Research and International Relations — Sergei Obiedkov


Deputy Dean for Methodical and Educational Work — Ilya Samonenko


Deputy Dean for Development, Finance and Administration — Irina Plisetskaya

Phone: +7 (495) 772-95-90 * 12332


125319, Moscow, 3 Kochnovsky Proezd (near metro station 'Aeroport'). 

Feb 22 – Feb 23
Регистрация открыта 
Mar 21 – Mar 23
Papers Submission Deadline: 15 January 2019 
Jun 12 – Jun 14
submission: Friday, 01 February 2019, notification: Friday, 15 February 2019 
Aug 26 – Aug 30
Registration and Poster Submission deadline — April 1, 2019 
Ontology-Mediated Queries: Combined Complexity and Succinctness of Rewritings via Circuit Complexity

Bienvenu M., Kikot S., Kontchakov R. et al.

Journal of the ACM. 2018. Vol. 65. No. 5. P. 28:1-28:51.

Randomized Block Cubic Newton Method
In press

Doikov Nikita, Richtarik P.

Proceedings of Machine Learning Research. 2018. No. 80. P. 1290-1298.

Particle-identification techniques and performance at LHCb in Run 2
In press

Hushchyn M., Chekalina V.

Nuclear Instruments and Methods in Physics Research, Section A: Accelerators, Spectrometers, Detectors and Associated Equipment. 2018. P. 1-2.

Observational evidence in favor of scale free evolution of sunspot groups

Shapoval A., Le Mouël J., Shnirman M. et al.

Astronomy and Astrophysics. 2018. Vol. 618. P. A183-1-A183-13.

Colloquium: Computational cognitive neuroscience: A brief primer. Speaker: Joseph MacInnes, HSE

Event ended

September 11, 18:10 – 19:30
Kochnovskii proezd, 3, room 205

Joseph MacInnes

Head of vision modelling lab / HSE

Computational cognitive neuroscience: A brief primer

Computational models in psychology and neuroscience share many algorithms with machine learning, machine vision and artificial intelligence, but the focus of the research is different. Where applied fields try to create algorithms that solve or automate a specific problem, computational modelling uses these algorithms to better understand fundamental workings of human brain and cognition. Rather than optimizing a new process, we try to simulate and understand an existing process. While computational modelling is still a growing field, there have emerged a number of contenders that perform very well in simulating various neural and cognitive processes. Diffusion models of decision making, salience models of vision and more recently deep learning models of object classification have all shown promise on their respective tasks. This talk will give an overview of a number of these models and discuss possible points of overlap with computer science and cognitive psychology.