The Use of Supervised Learning Models in Studying Agonistic Behavior and Communication in Weakly Electric Fish

Frontiers in Behavioral Neuroscience

卷:15

Jacob Engelmann¹  Sarah Nicola Jung¹  Hendrik Herzog²  Federico Pedraja³ 

[1] Active Sensing, Faculty of Biology, Bielefeld University, Bielefeld, Germany;

[2] Department of Neuroethology/Sensory Ecology, Institute for Zoology, University of Bonn, Bonn, Germany;

[3] Department of Neuroscience, Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, United States;

关键词: supervised learning;    agonistic behavior;    weakly electric fish;    passive electric image;    active electric image;   

DOI  :  10.3389/fnbeh.2021.718491

来源: DOAJ

【 摘 要 】

Despite considerable advances, studying electrocommunication of weakly electric fish, particularly in pulse-type species, is challenging as very short signal epochs at variable intervals from a few hertz up to more than 100 Hz need to be assigned to individuals. In this study, we show that supervised learning approaches offer a promising tool to automate or semiautomate the workflow, and thereby allowing the analysis of much longer episodes of behavior in a reasonable amount of time. We provide a detailed workflow mainly based on open resource software. We demonstrate the usefulness by applying the approach to the analysis of dyadic interactions of Gnathonemus petersii. Coupling of the proposed methods with a boundary element modeling approach, we are thereby able to model the information gained and provided during agonistic encounters. The data indicate that the passive electrosensory input, in particular, provides sufficient information to localize a contender during the pre-contest phase, fish did not use or rely on the theoretically also available sensory information of the contest outcome-determining size difference between contenders before engaging in agonistic behavior.

【 授权许可】

Unknown