Tools Development and Open Source Neuroscience
Author: Valentín Agulló | email: valentin_a_98@hotmail.com
Valentín Agulló 1°, Nicolás Martorell 1°, Violeta Medan 1°
1° Instituto de Fisiología Biología Molecular y Neurociencias, CONICET-UBA
Our behavior determines how we interact with the environment. However, understanding of how the brain processes information to organize behavior is still incomplete. Critically, detection of dangerous stimuli (predators) determine the escape (avoiding death). Here, we recorded responses of zebrafish larvae to different visual and auditory stimuli (separately or in combination) and analyzed the kinematics of the locomotor response. We developed a tracking algorithm based on OpenCV (Python) followed by computational analysis to extract different quantitative parameters of the trajectory. Our goal was to find relevant variables to perform an automatic categorization of behavioral escape patterns. For this, locomotor activity was segmented into discrete events by defining thresholds taken in the phase space of linear and angular velocity. This allowed separation of slow events (such as normal swimming bouts, n?8900) and fast events (FE, n=588). Next we compared maximum speeds and accelerations, maximum twist angle, etc. between a subset of FE categorized by an observer as C-start (oCS, n=167) and all FE. Importantly, the method labeled >95% of all oCS as FE. Results show that 1) rapid escape events can be automatically classified based on a low number of kinetic variables, 2) auditory escapes have higher maximum velocities than visual escapes, 3) audiovisual escapes show a bimodal distribution and 4) substantial variability in FE requires additional parameters to be categorized.