A Computational Model Integrating Multiple Phenomena on Cued Fear Conditioning, Extinction, and Reinstatement

Abstract

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 25 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.

From metaphor to computation: Constructing the potential landscape for multivariate psychological formal models

“…There is also a possible theoretical explanation for this modification, namely fear extinction. After the association (arousal schema, AS) between physical arousal (A) and perceived threat (PT) is learned, even when there are no new fear-inducing events, the strength of this association still decreases (Mattera et al, 2020;Milad & Quirk, 2012). It is important to emphasize that the decision to add a negative value to the AS time derivative in the AS extinction modification was inspired by the potential landscape of the model, not based on a theoretical analysis.…”

Neural Circuits Underlying Social Fear in Rodents: An Integrative Computational Model

“…where τ is the time constant, I is the external input to the unit (representing the “defeat,” the “conspecific,” and the “context;” see Figure 1 ), w post,pre is the connection weight between the presynaptic unit pre and unit post . F is the activation of the unit, computed with the hyperbolic tangent function tanh ( x ), and represents the firing rate of a population of neurons (Burgos and Murillo-Rodŕıguez, 2007 ; Moustafa et al, 2013 ; Carrere and Alexandre, 2015 ; Mannella et al, 2016 ; Bennett et al, 2019 ; Mattera et al, 2020 ):…”

“…In the construction of the mPFC, we followed the standard approach of the top-down models (John et al, 2013 ; Moustafa et al, 2013 ; Li et al, 2016 ; Oliva et al, 2018 ; Bennett et al, 2019 ; Mattera et al, 2020 ), abstracting over the layered circuits and intra-cortical connectivity of the cortex. In particular, since ours is a system-level model considering several areas, it was not possible to include in the model all the known connections linking those areas.…”

“…The weights of the connections of the two models (listed in Supplementary Table S2 , together with the literature supporting their existence) were manually adjusted until we found a configuration sufficient to reproduce all the target experiments (Mattera et al, 2020 ). To test the robustness of the results to the modification of the parameters, we performed a sensitivity analysis (see Supplementary Material and Section 3.5).…”

“…To our knowledge, while classical fear conditioning has been widely investigated with the use of computational models (Burgos and Murillo-Rodŕıguez, 2007 ; Mannella et al, 2008 ; Krasne et al, 2011 ; Anastasio, 2013 ; John et al, 2013 ; Moustafa et al, 2013 ; Carrere and Alexandre, 2015 ; Li et al, 2016 ; Bennett et al, 2019 ; Mattera et al, 2020 ), the model proposed here is the first to account for social avoidance. The model is based on a number of hypotheses, grounded on literature, concerning the involved brain areas, neural populations, and connections between them underlying social avoidance.…”