The neuronal systems that promote protective defensive behaviours have been studied extensively using Pavlovian conditioning. In this paradigm, an initially neutral-conditioned stimulus is paired with an aversive unconditioned stimulus leading the subjects to display behavioural signs of fear. Decades of research into the neural bases of this simple behavioural paradigm uncovered that the amygdala, a complex structure comprised of several interconnected nuclei, is an essential part of the neural circuits required for the acquisition, consolidation and expression of fear memory. However, emerging evidence from the confluence of electrophysiological, tract tracing, imaging, molecular, optogenetic and chemogenetic methodologies, reveals that fear learning is mediated by multiple connections between several amygdala nuclei and their distributed targets, dynamical changes in plasticity in local circuit elements as well as neuromodulatory mechanisms that promote synaptic plasticity. To uncover these complex relations and analyse multi-modal data sets acquired from these studies, we argue that biologically realistic computational modelling, in conjunction with experiments, offers an opportunity to advance our understanding of the neural circuit mechanisms of fear learning and to address how their dysfunction may lead to maladaptive fear responses in mental disorders.

人们已经利用巴甫洛夫条件作用对促进保护性防御行为的神经系统进行了广泛的研究。在这种范式中，最初的中性条件刺激与厌恶的无条件刺激配对，导致受试者表现出恐惧的行为迹象。对这种简单行为范式的神经基础进行了数十年的研究发现，杏仁核是一种由多个相互连接的核组成的复杂结构，是获取、巩固和表达恐惧记忆所需的神经回路的重要组成部分。然而，来自电生理学、束追踪、成像、分子、光遗传学和化学遗传学方法学汇合的新证据表明，恐惧学习是由几个杏仁核及其分布目标之间的多重连接以及局部电路元件可塑性的动态变化介导的。作为促进突触可塑性的神经调节机制。为了揭示这些复杂的关系并分析从这些研究中获得的多模态数据集，我们认为生物现实计算模型与实验相结合，提供了一个机会来加深我们对恐惧学习的神经回路机制的理解，并解决它们如何功能障碍可能会导致精神障碍中适应不良的恐惧反应。