UCL Sainsbury Wellcome Centre for Neural Circuits and Behaviour, London W1T 4JG, United Kingdom; email:
Department of Psychology, The University of Sheffield, Sheffield S1 2LT, United Kingdom; email:
Annu Rev Neurosci. 2020 Jul 8;43:417-439. doi: 10.1146/annurev-neuro-100219-122527. Epub 2020 Apr 7.
Escape is one of the most studied animal behaviors, and there is a rich normative theory that links threat properties to evasive actions and their timing. The behavioral principles of escape are evolutionarily conserved and rely on elementary computational steps such as classifying sensory stimuli and executing appropriate movements. These are common building blocks of general adaptive behaviors. Here we consider the computational challenges required for escape behaviors to be implemented, discuss possible algorithmic solutions, and review some of the underlying neural circuits and mechanisms. We outline shared neural principles that can be implemented by evolutionarily ancient neural systems to generate escape behavior, to which cortical encephalization has been added to allow for increased sophistication and flexibility in responding to threat.
逃避是研究最多的动物行为之一,并且有丰富的规范理论将威胁属性与逃避行为及其时间联系起来。逃避的行为原则在进化上是保守的,依赖于分类感觉刺激和执行适当动作等基本计算步骤。这些是一般适应行为的常见构建块。在这里,我们考虑实施逃避行为所需的计算挑战,讨论可能的算法解决方案,并回顾一些潜在的神经回路和机制。我们概述了可以通过进化上古老的神经系统来实现逃避行为的共享神经原则,皮质大脑化已被添加到其中,以允许对威胁做出更高的复杂性和灵活性的反应。