Linear Systems Theoretic Approach to Interpretation of Spatial and Temporal Weights in Compact CNNs: Monte-Carlo Study

Artur Petrosyan, Mikhail Lebedev, Alexey Ossadtchi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Interpretation of the neural networks architectures for decoding the signals of the brain usually reduced to the analysis of spatial and temporal weights. We propose a theoretically justified method of their interpretation within the simple architecture based on a priori knowledge of the subject area. This architecture is comparable in decoding quality to the winner of the BCI IV competition and allows for automatic engineering of physiologically meaningful features. To demonstrate the operation of the algorithm, we performed Monte Carlo simulations and received a significant improvement in the restoration of patterns for different noise levels and also investigated the relation between the decoding quality and patterns reconstruction fidelity.

Original languageEnglish
Title of host publicationBrain-Inspired Cognitive Architectures for Artificial Intelligence
Subtitle of host publicationBICA*AI 2020 - Proceedings of the 11th Annual Meeting of the BICA Society
EditorsAlexei V. Samsonovich, Ricardo R. Gudwin, Alexandre da Simões
PublisherSpringer Science and Business Media Deutschland GmbH
Pages365-370
Number of pages6
ISBN (Print)9783030655952
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event11th Annual International Conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence, BICA*AI 2020 - Natal, Brazil
Duration: 10 Nov 202014 Nov 2020

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1310
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference11th Annual International Conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence, BICA*AI 2020
Country/TerritoryBrazil
CityNatal
Period10/11/2014/11/20

Keywords

  • Deep learning
  • ECoG
  • Limb kinematics decoding
  • Machine learning
  • Monte Carlo
  • Weights interpretation

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