Advances in non-invasive tracking of wave-type electric fish in natural and laboratory settings

Tracking spatial patterns and daily modulation of behavior in a natural population of the pulse-type weakly electric fish, Gymnotus omarorum

Tracking individual spatial and activity-rest patterns in natural populations is challenging because it is seldom possible to monitor individual-specific traits continuously. The continuous emission of electric signals (EODs) by weakly electric fish provides a unique opportunity to do this. We present a cutting-edge technique, arrays of electrodes connected to low-cost amplifiers and tracking algorithm, to provide the individual identification of pulse-type weakly electric fish in the wild. Based only on EOD recordings of individuals of Gymnotus omarorum, we show that (1) there are more fish in core than in edge zones; (2) transitions into and out of the recording sites were more frequent at night, and (3) resident fish show robust nocturnal increases of EOD rate likely associated with daily variations of water temperature. This experimental approach can be extended to other species to improve our understanding of the behavior, ecology, and well-being of electric fish in natural environments.

Supervised learning algorithm for analysis of communication signals in the weakly electric fish Apteronotus leptorhynchus

Signal analysis plays a preeminent role in neuroethological research. Traditionally, signal identification has been based on pre-defined signal (sub-)types, thus being subject to the investigator's bias. To address this deficiency, we have developed a supervised learning algorithm for the detection of subtypes of chirps-frequency/amplitude modulations of the electric organ discharge that are generated predominantly during electric interactions of individuals of the weakly electric fish Apteronotus leptorhynchus. This machine learning paradigm can learn, from a 'ground truth' data set, a function that assigns proper outputs (here: time instances of chirps and associated chirp types) to inputs (here: time-series frequency and amplitude data). By employing this artificial intelligence approach, we have validated previous classifications of chirps into different types and shown that further differentiation into subtypes is possible. This demonstration of its superiority compared to traditional methods might serve as proof-of-principle of the suitability of the supervised machine learning paradigm for a broad range of signals to be analyzed in neuroethology.

A perspective on neuroethology: what the past teaches us about the future of neuroethology

For 100 years, the Journal of Comparative Physiology-A has significantly supported research in the field of neuroethology. The celebration of the journal's centennial is a great time point to appreciate the recent progress in neuroethology and to discuss possible avenues of the field. Animal behavior is the main source of inspiration for neuroethologists. This is illustrated by the huge diversity of investigated behaviors and species. To explain behavior at a mechanistic level, neuroethologists combine neuroscientific approaches with sophisticated behavioral analysis. The rapid technological progress in neuroscience makes neuroethology a highly dynamic and exciting field of research. To summarize the recent scientific progress in neuroethology, I went through all abstracts of the last six International Congresses for Neuroethology (ICNs 2010-2022) and categorized them based on the sensory modalities, experimental model species, and research topics. This highlights the diversity of neuroethology and gives us a perspective on the field's scientific future. At the end, I highlight three research topics that may, among others, influence the future of neuroethology. I hope that sharing my roots may inspire other scientists to follow neuroethological approaches.