Computational Geometric Tools for Modeling Inherent Variability in Animal Behavior

ABSTRACT A fundamental challenge for behavioral neuroscientists is to represent inherent variability among animals accurately without compromising the ability to quantify differences between conditions. We developed two new methods that apply curve and shape alignment techniques to address this issue. As a proof-of-concept we applied these methods to compare normal or alarmed behavior in pairs of medaka (Oryzias latipes). The curve alignment method we call Behavioral Distortion Distance (BDD) revealed that alarmed fish display less predictable swimming over time, even if individuals incorporate the same action patterns like immobility, sudden changes in swimming trajectory, or changing their position in the water column. The Conformal Spatiotemporal Distance (CSD) technique on the other hand revealed that, in spite of the unpredictability, alarmed individuals share an overall swim pattern, possibly accounting for the widely held notion of “stereotypy” in alarm responses. More generally, we propose that these new applications of known computational geometric techniques are useful in combination to represent, compare, and quantify complex behaviors consisting of common action patterns that differ in duration, sequence, or frequency.