  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.
