Fuzzy differential inclusion in neural modeling

Sina Tafazoli, Mohammad Bagher Menhaj

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Dynamical systems theory has helped brain scientists to cope better with brain complexity. In this paper, we proposed a novel approach to include uncertainty in dynamical system describing brain function such as one neuron or coupled neurons. Fuzzy dynamical systems represented by a set of fuzzy differential inclusions (FDI) are very convenient tools for modeling and simulation of various uncertain systems. We used fuzzy differential inclusion in modeling neural responses in several types of neurons. We showed that our results are very similar to real experimental data showing variability in neural responses. Further, we have shown that FDI has advantage in comparison with modeling uncertainty in neural systems with Stochastic Differential Equations (SDEs).

Original languageEnglish (US)
Title of host publication2009 IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2009 - Proceedings
Pages70-77
Number of pages8
DOIs
StatePublished - 2009
Externally publishedYes
Event2009 IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2009 - Nashville, TN, United States
Duration: Mar 30 2009Apr 2 2009

Publication series

Name2009 IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2009 - Proceedings

Conference

Conference2009 IEEE Symposium on Computational Intelligence in Control and Automation, CICA 2009
Country/TerritoryUnited States
CityNashville, TN
Period3/30/094/2/09

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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