Conditioning, extinction, and reinstatement are fundamental learning processes of animal adaptation, also strongly involved in
human pathologies such as post-traumatic stress disorder, anxiety, depression, and dependencies.
Cued fear conditioning, extinction, restatement, and systematic manipulations of the underlying brain amygdala and medial
prefrontal cortex, represent key experimental paradigms to study such processes.
Numerous empirical studies have revealed several aspects and the neural systems and plasticity underlying them, but at the
moment we lack a comprehensive view.
Here we propose a computational model based on firing rate leaky units that contributes to such integration by accounting for
twenty-five different experiments on fear conditioning, extinction, and restatement, on the basis of a single neural architecture
having a structure and plasticity grounded in known brain biology.
This allows the model to furnish three novel contributions to understand these open issues:
(a) the functioning of the central and lateral amygdala system supporting conditioning;
(b) the role played by the endocannabinoids system in within- and between-session extinction;
(c) the formation of three important types of neurons underlying fear processing, namely fear, extinction, and persistent neurons.
The model integration of the results on fear conditioning goes substantially beyond what was done in previous models.
A computational model integrating multiple phenomena on cued fear conditioning, extinction, and reinstatement
Publication type:
Articolo
Publisher:
Frontiers Research Foundation,, Lausanne , Svizzera
Source:
Frontiers in systems neuroscience 14 (2020): 1–22. doi:10.3389/fnsys.2020.569108
info:cnr-pdr/source/autori:Mattera A, Pagani M, Baldassarre G/titolo:A computational model integrating multiple phenomena on cued fear conditioning, extinction, and reinstatement/doi:10.3389/fnsys.2020.569108/rivista:Frontiers in systems neuroscience/anno
Date:
2020
Resource Identifier:
http://www.cnr.it/prodotto/i/429263
https://dx.doi.org/10.3389/fnsys.2020.569108
info:doi:10.3389/fnsys.2020.569108
Language:
Eng