An Adaptive Cognitive Temporal-Causal Model for Extreme Emotion Extinction Using Psilocybin

Seyed Sahand Mohammadi Ziabari*, Jan Treur

*Corresponding author for this work

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

Abstract

In this paper, an adaptive cognitive temporal-causal model using psilocybin for a reduction in extreme emotion is presented. Extreme emotion has an effect on some brain components such as visual cortex, auditory cortex, gustatory cortex, and somatosensory cortex as well as motor cortex such as primary motor cortex, and premotor cortex. Neuroscientific literature reviews show that using psilocybin has a significant effect mostly on two brain components, cerebral cortex, and thalamus. Network-oriented modeling via temporal-causal network-oriented modeling is presented to show the influences of using psilocybin on the cognitive part of the body, same as the brain components. Hebbian learning used to show the adaptivity and learning section of the presented model.

Original languageEnglish
Title of host publicationComputational Statistics and Mathematical Modeling Methods in Intelligent Systems
Subtitle of host publicationProceedings of 3rd Computational Methods in Systems and Software 2019
EditorsRadek Silhavy, Petr Silhavy, Zdenka Prokopova
PublisherSpringer
Pages176-186
Number of pages11
Volume2
ISBN (Electronic)9783030313623
ISBN (Print)9783030313616
DOIs
Publication statusPublished - 2019
Event3rd Computational Methods in Systems and Software, CoMeSySo 2019 - Zlin, Czech Republic
Duration: 10 Sep 201912 Sep 2019

Publication series

NameAdvances in Intelligent Systems and Computing
Volume1047
ISSN (Print)2194-5357
ISSN (Electronic)2194-5365

Conference

Conference3rd Computational Methods in Systems and Software, CoMeSySo 2019
CountryCzech Republic
CityZlin
Period10/09/1912/09/19

Keywords

  • Extreme emotion
  • Network-oriented modeling
  • Psilocybin
  • Temporal-causal network

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