Using a biomimetric neural net to model dolphin echolocation

A biomimetic neural network was used to model the ability of a bottle nosed dolphin to recognize aspect-dependent geometric objects. Each echo train was recorded and an Integrator Gateway Network (IGN) was trained to discriminate among the objects using spectra of the object echoes. The IGN classifies objects using an average-like sum of the spectra from successive echoes. However, combining echoes may reduce classification accuracy if the spectra vary from echo to echo. The dolphin and the IGN learned to recognize the geometric objects, even though orientation was free to vary. The process of recognition using cumulated echoes was robust with respect to nonstationary raw input. The results were interpreted as evidence for the formation of aspect-independent representations of the objects