An algorithmic approach to natural behavior

Journal article : Review

Uncovering the mechanisms and implications of natural behavior is a goal that unites many fields of biology. Yet, the diversity, flexibility, and multi-scale nature of these behaviors often make understanding elusive. Here, we review studies of animal pursuit and evasion — two special classes of behavior where theory-driven experiments and new modeling techniques are beginning to uncover the general control principles underlying natural behavior. A key finding of these studies is that intricate sequences of pursuit and evasion behavior can often be constructed through simple, repeatable rules that link sensory input to motor output: we refer to these rules as behavioral algorithms. Identifying and mathematically characterizing these algorithms has led to important insights, including the discovery of guidance rules that attacking predators use to intercept mobile prey, and coordinated neural and biomechanical mechanisms that animals use to avoid impending collisions. Here, we argue that algorithms provide a good starting point for studies of natural behavior more generally. Rather than beginning at the neural or ecological levels of organization, we advocate starting in the middle, where the algorithms that link sensory input to behavioral output can provide a solid foundation from which to explore both the implementation and the ecological outcomes of behavior. We review insights that have been gained through such an algorithmic approach to pursuit and evasion behaviors. From these, we synthesize theoretical principles and lay out key modeling tools needed to apply an algorithmic approach to the study of other complex natural behaviors.

Authors: Hein, AM; Altshuler, DL; Cade, DE; Liao, JC; Martin, BT

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Cell Press
• Journal: Current Biology
• Volume: 30
• Issue: 11
• Pages: R663-R675
• Publication date: 2020-06-08
• Acceptance date: 2020-03-31
• DOI: 10.1016/j.cub.2020.04.018
• EISSN: 1879-0445
• ISSN: 0960-9822
• Language: English
• Keywords: FFR
• Subtype: Review