The study of exotic sensory systems, such as electroreception in fish, echolocation in bats, and sound localization in owls, has revealed general principles of neuronal organization that are frequently present but more difficult to discern in other animals and humans. Weakly electric fish are an exceptional model system to study sensory acquisition, neuronal information processing, and sensory-motor integration. These animals detect nearby objects by sensing object-induced distortions in their electric organ discharge (EOD) electric field (reviewed in Bastian 1994; Carr 1990; Bullock and Heiligenberg 1986). Sensory electroreceptor organs, distributed across the fish's body, are acutely sensitive to small changes in transdermal voltage, which constitute an "electric image" of the object. We have investigated how electric fish might identify object features, such as size, shape, location, and impedance, from the object's electric images (Fig. 1), For example, how might a fish differentiate between a large, distant object and a small, nearby one; or a large object with impedance similar to water, and a small object with greater impedance difference? To resolve these questions, we constructed detailed and accurate simulations of the electric images of spheres and ellipsoids placed in EOD fields (Rasnow 1996). Electric images were computed analytically by assuming the measured EOD field was uniform around the object. Measured electric images of large metal spheres verified the simulations, and revealed their robustness to this assumption. In this paper, we summarize the algorithms for electrolocation presented by Rasnow (1996) and propose a plausible neural implementation of these algorithms in the fish's hind and midbrain.
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