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

## TL;DR

This paper introduces a machine learning method to analyze communication signals in a species of electric fish, improving on traditional biased approaches.

## Contribution

A supervised learning algorithm is developed for unbiased classification of electric fish communication signals into subtypes.

## Key findings

- The algorithm validated previous chirp classifications and identified further subtypes.
- The method outperforms traditional approaches in signal analysis for neuroethology.
- It demonstrates the potential of supervised learning for analyzing diverse neurobiological signals.

## Abstract

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.