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.

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