  A computational theory for classification of natural biosonar targets is
developed based on the properties of an example stimulus ensemble. An extensive
set of echoes (84 800) from four different foliages was transcribed into a
spike code using a parsimonious model (linear filtering, half-wave
rectification, thresholding). The spike code is assumed to consist of time
differences (interspike intervals) between threshold crossings. Among the
elementary interspike intervals flanked by exceedances of adjacent thresholds,
a few intervals triggered by disjoint half-cycles of the carrier oscillation
stand out in terms of resolvability, visibility across resolution scales and a
simple stochastic structure (uncorrelatedness). They are therefore argued to be
a stochastic analogue to edges in vision. A three-dimensional feature vector
representing these interspike intervals sustained a reliable target
classification performance (0.06% classification error) in a sequential
probability ratio test, which models sequential processing of echo trains by
biological sonar systems. The dimensions of the representation are the first
moments of duration and amplitude location of these interspike intervals as
well as their number. All three quantities are readily reconciled with known
principles of neural signal representation, since they correspond to the center
of gravity of excitation on a neural map and the total amount of excitation.
