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      The Neural Basis of Escape Behavior in Vertebrates

      1 , 2
      Annual Review of Neuroscience
      Annual Reviews

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          Abstract

          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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          Most cited references3

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          The hippocampus as a cognitive map

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            Neurons, Networks, and Motor Behavior

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              The Perception of the Visual World

              Gibson JJ. (1974)
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                Author and article information

                Journal
                Annual Review of Neuroscience
                Annu. Rev. Neurosci.
                Annual Reviews
                0147-006X
                1545-4126
                July 08 2020
                July 08 2020
                : 43
                : 1
                : 417-439
                Affiliations
                [1 ]UCL Sainsbury Wellcome Centre for Neural Circuits and Behaviour, London W1T 4JG, United Kingdom;
                [2 ]Department of Psychology, The University of Sheffield, Sheffield S1 2LT, United Kingdom;
                Article
                10.1146/annurev-neuro-100219-122527
                32259462
                ede94d55-35d1-411f-95d5-c12063d3618b
                © 2020
                History

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