Individual discrimination of freely swimming pulse-type electric fish from electrode array recordings

TL;DR

This paper presents a computational method that accurately identifies individual electric fish from electrode recordings by analyzing their unique electrical discharge waveforms using advanced signal processing and machine learning techniques.

Contribution

The authors develop a novel approach combining Fourier and wavelet transforms with support vector machines to distinguish individual fish EODs in multi-fish recordings, addressing a key challenge in behavioral neuroethology.

Findings

Correctly assigns EODs to each fish with only two errors per million discharges.

Effective in dyads of freely swimming pulse-type electric fish.

Validates method with extensive tests on Gymnotus sp.

Abstract

Pulse-type weakly electric fishes communicate through electrical discharges with a stereotyped waveform, varying solely the interval between pulses according to the information being transmitted. This simple codification mechanism is similar to the one found in various known neuronal circuits, which renders these animals as good models for the study of natural communication systems, allowing experiments involving behavioral and neuroethological aspects. Performing analysis of data collected from more than one freely swimming fish is a challenge since the detected electric organ discharge (EOD) patterns are dependent on each animal's position and orientation relative to the electrodes. However, since each fish emits a characteristic EOD waveform, computational tools can be employed to match each EOD to the respective fish. In this paper we describe a computational method able to recognize fish EODs from dyads using normalized feature vectors obtained by applying Fourier and dual-tree complex wavelet packet transforms. We employ support vector machines as classifiers, and a continuity constraint algorithm allows us to solve issues caused by overlapping EODs and signal saturation. Extensive validation procedures with Gymnotus sp. showed that EODs can be assigned correctly to each fish with only two errors per million discharges.