Transforming echoes into pseudo-action potentials for classifying plants.
Animals perceive their environment by converting sensory stimuli into action potentials, or temporal point processes, that are interpreted by the brain. This paper investigates the information content of point processes extracted from echoes from in situ plants in an effort to understand how bats recognize landmarks in the field. A mobile sonar converts echoes into biologically similar temporal point processes. termed pseudo-action potentials (PAPs), whose inter-PAP interval relates to echo amplitude. The sonar forms a sector scan of an object to produce a spatial-temporal PAP field. Classifier neurons apply delays and coincidence detection to the PAP field to identify three distinct echo types, glints, blobs, and fuzz, which characterize plant features. Glints are large amplitude echoes exhibiting coherence over successive echoes in the sector scan, typically produced by favorably oriented isolated specular reflectors. Blobs are large echoes lacking coherence, typically bordering glints or formed by collections of interfering reflectors. Fuzz represents weak echoes, typically produced by collection of weak scatterers or by reflectors on the beam periphery. A small mirror reflector models a flat leaf surface and motivates the glint criteria. Classifiers are applied to experimental data from two types of tree trunks, a glint-producing sycamore (Platanus occidenatalis) and a glint-absent Norway maple (Acer platanoides) and two plants, a glint-producing rhododendron (Rhododendron maximus) and a glint-absent yew (Taxus media). We speculate that our narrow-band sonar models the activity of a single frequency bin in the frequency-modulated (FM) sweep emitted by bats, and that one function of the frequency bins in the FM sweep is to form a sector scan of the environment.
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Department of Electrical Engineering, Yale University, New Haven, Connecticut 06520-8284, USA.
D000818:Animals; D002685:Chiroptera / Q000502:physiology*; D004455:Echolocation / Q000502:physiology*; D006198:Hair Cells, Auditory / Q000502:physiology; D016571:Neural Networks, Computer; D010944:Plants / Q000145:classification*; D013018:Sound Spectrography