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Pulse-type weakly electric fishes communicate through electrical dischargeswith a stereotyped waveform, varying solely the interval between pulsesaccording to the information being transmitted. This simple codificationmechanism is similar to the one found in various known neuronal circuits, whichrenders these animals as good models for the study of natural communicationsystems, allowing experiments involving behavioral and neuroethologicalaspects. Performing analysis of data collected from more than one freelyswimming fish is a challenge since the detected electric organ discharge (EOD)patterns are dependent on each animal's position and orientation relative tothe 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 EODsfrom dyads using normalized feature vectors obtained by applying Fourier anddual-tree complex wavelet packet transforms. We employ support vector machinesas classifiers, and a continuity constraint algorithm allows us to solve issuescaused by overlapping EODs and signal saturation. Extensive validationprocedures with Gymnotus sp. showed that EODs can be assigned correctly to eachfish with only two errors per million discharges.

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