Unsupervised electric signal separation for linking behavior and electrocommunication in Gnathonemus petersii

The transfer of information between individuals is fundamental to living systems and requires comprehensive research in various species. Weakly electric fish, Gnathonemus petersii, provides a unique model organism for such investigations due to its advanced electrocommunication via electric organ discharges (EODs). As separating EODs from multiple individuals remains challenging, we developed an unsupervised approach for EOD separation in two free-swimming individuals. Using continuous wavelet transform, t-distributed Stochastic Neighbor Embedding, and hierarchical clustering, we achieved accurate discrimination of EODs without the necessity of any training data. This approach overcomes the supervised algorithms based on previously published methods in accuracy and computational efficiency, simplifies experimental procedures, and supports animal well-being by reducing the number of required measurements. We applied our separation approach in a dyadic fish model, where ketamine was used to induce schizophrenia-like behavior in one fish. We confirmed the ketamine-induced alteration of the intrinsic relationship between locomotion and EOD signaling. Moreover, while ketamine-induced changes in locomotion were socially transferred, correlated changes in EOD signaling were not observed between dyad members, which may be interpreted as a communication deficit. Additionally, we introduced two techniques for EOD sonification, facilitating exploratory analysis of EOD sequences. These advancements lay the groundwork for future studies of EOD-based communication, highlighting the potential of Gnathonemus petersii in neuroethological, psychopharmacological, and translational research.