Computational modeling of fear and stress responses: validation using consolidated fear and stress protocols

Abstract

Dysfunction in fear and stress responses is intrinsically linked to various neurological diseases, including anxiety disorders, depression, and Post-Traumatic Stress Disorder. Previous studies using in vivo models with Immediate-Extinction Deficit (IED) and Stress Enhanced Fear Learning (SEFL) protocols have provided valuable insights into these mechanisms and aided the development of new therapeutic approaches. However, assessing these dysfunctions in animal subjects using IED and SEFL protocols can cause significant pain and suffering. To advance the understanding of fear and stress, this study presents a biologically and behaviorally plausible computational architecture that integrates several subregions of key brain structures, such as the amygdala, hippocampus, and medial prefrontal cortex. Additionally, the model incorporates stress hormone curves and employs spiking neural networks with conductance-based integrate-and-fire neurons. The proposed approach was validated using the well-established Contextual Fear Conditioning paradigm and subsequently tested with IED and SEFL protocols. The results confirmed that higher intensity aversive stimuli result in more robust and persistent fear memories, making extinction more challenging. They also underscore the importance of the timing of extinction and the significant influence of stress. To our knowledge, this is the first instance of computational modeling being applied to IED and SEFL protocols. This study validates our computational model's complexity and biological realism in analyzing responses to fear and stress through fear conditioning, IED, and SEFL protocols. Rather than providing new biological insights, the primary contribution of this work lies in its methodological innovation, demonstrating that complex, biologically plausible neural architectures can effectively replicate established findings in fear and stress research. By simulating protocols typically conducted in vivo-often involving significant pain and suffering-in an insilico environment, our model offers a promising tool for studying fear-related mechanisms. These findings support the potential of computational models to reduce the reliance on animal testing while setting the stage for new therapeutic approaches.

Keywords: Immediate Extinction Deficit (IED); biologically plausible models; computational modeling; contextual fear conditioning; fear extinction; neural architecture; stress models; stress-enhanced fear learning (SEFL).

Figure 1

Architecture of the proposed model. Red rectangles and lines represent inhibitory connections, green rectangles and lines represent excitatory connections, blue lines represent stress responses, and dotted lines represent plastic connections. In addition to the elucidated information within the text, pertinent details regarding the number of neurons, connections, and referenced works for each region utilized in the proposed model are provided throughout the text.

Figure 2

Neuroendocrine responses to stress. Noradrenaline (NE) levels increase until approximately twenty minutes and decrease until sixty minutes. Corticosteroid levels also increase exponentially and return to baseline after one to 2 h. The x-axis specifies the time in minutes.

Figure 3

Average level of freezing (%) for Contextual Fear Conditioning: The Figure illustrates the sequential protocol of (CFC 1) fear acquisition, (CFC 2) fear extinction, (CFC 3) renewal, and repetition of fear extinction. In Phase CFC 1, the acquisition occurs for AX+. In Phase CFC 2, Context “B” for extinction is introduced, denoted as BX−. In Phase CFC 3, Group 1 presents renewal and Group 2 with repetition of extinction.

Figure 4

Average freezing level (%) for fear responses at different magnitudes during the acquisition phase. The Figure presents a simulation that captures fear responses at different magnitudes during the acquisition phase. In Phase SM-1, three distinct groups are subjected to different intensities of electric shock in AX+: (A) Group 1 receives a two shocks, (B) Group 2 receives ten shocks, (C) Group 3 receives twenty shocks, and (D) Group 4, thirty shocks. The simulation advances to Phase SM-2 with fifteen cycles in BX−.

Figure 5

Average level of freezing (%) for fear responses obtained for SEFL. (A) Group 1 goes through Phase SEFL 1, fifteen cycles in A, in Phase SEFL 2, one cycle in B, and in Phase SEFL 3, one cycle in B. (B) Group 2 proceeds with Phase SEFL 1, fifteen cycles in A, Phase SEFL 2, one cycle in B+, and Phase SEFL 3, one cycle in B. (C) Group 3 experiences Phase SEFL 1, fifteen cycles in A+, Phase SEFL 2, one cycle in B, and Phase SEFL 3, one cycle in B. (D) Group 4 undergoes Phase SEFL 1, fifteen cycles at A+, Phase SEFL 2, one cycle at B+, and Phase SEFL 3, one cycle at B.

Figure 6

Average level of freezing (%) for fear responses obtained for “Shock stress (SS) must precede fear conditioning.” All groups undergo testing in Context B in Phase SS 3 and Context A in Phase SS 4. (A) Group 1 goes through Phase SS 1, one cycle in Context B, and Phase SS 2, 15 cycles in Context A. (B) Group 2 undergoes Phase SS 1, one cycle in Context B, and 15 cycles with shock in Context A. (C) Group 3 proceeds with Phase SS 1, one shock in Context B, and Phase SS 2, 15 cycles in Context A. (D) Group 4 experiences Phase SS 1, one shock in Context B, and Phase SS 2, 15 shocks in Context A.

Figure 7

Average freezing level (%) for IED model. This experiment establishes the core parameters, centering on fear acquisition and extinction. It contrasts fear responses among (A) immediate, (B) delayed, (C) non-immediate, and (D) non-delayed extinction groups.