In this paper, a computational model is presented to simulate the effect of Eye Movement Desensi-tization and Reprocessing (EMDR) therapy on persons affected by a Post-Traumatic Stress Disor-der (PTSD). The simulation is based on an adaptive temporal-causal network modelling approach. Adaptiveness is achieved using network reification, to model plasticity based on the Hebbian Learning principle and metaplasticity. During EMDR therapy, within the brain resource competi-tion occurs, which helps to improve stress regulation. More specifically, eye-movement interven-tion causes competition between parietal networks and the amygdala, due to which they negatively affect each other’s activation. Psychological traumas impair (extinction) learning by so-called ‘neg-ative metaplasticity’. EMDR is functional in shifting this back to ‘positive metaplasticity’. This re-vitalizes extinction learning and memory reconsolidation. The introduced adaptive network model and its simulation confirms the functionality of the neural processes and the effective treatment re-sults of EMDR.
|Name||Advances in Intelligent Systems and Computing|
|Conference||11th Annual International Conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence, BICA*AI 2020|
|Period||10/11/20 → 14/11/20|
- Computational model
- Hebbian learning
- Memory reconsolidation
- Resource competition