Electric images of two low resistance objects in weakly electric fish
Introduction
Electroreceptive fish detect nearby objects by processing the information contained in the pattern of electric currents through their skin. These currents, in weakly electric fish, result from a self-generated field (the electric organ discharge), depending on the relative positions of different parts of the animal (as for example body bending, tail position, etc.) and the electrical properties of the surrounding medium. The electric image can be defined as the pattern of transepidermal voltage (Bastian, 1986, Bell, 1989). From this image the brain constructs a representation of the external world. To understand electrolocation it is necessary to know how electric images of objects are generated in a complex environment. Electric images of isolated objects have been measured in certain specific cases (Aguilera et al., 2001, Hoshimiya et al., 1980, Rasnow, 1996; Rasnow et al., 1993; Rasnow and Bower, 1996, von der Emde and Bleckmann, 1992, von der Emde et al., 1998), but this approach, having the strength of empirical data, lacks the flexibility for describing different scenes and circumstances. Complementing these experimental studies, theoretical analysis of image generation has yielded realistic models that predict with acceptable accuracy the electrosensory stimulus (Assad, 1997, Budelli and Caputi, 2000; Caputi and Budelli, 1995; Caputi et al., 1998, Heiligenberg, 1973, Lissmann and Machin, 1958, Rasnow, 1996). When the image is calculated by these computational models, an unique object is placed either in an infinite environment or in a tank. Experimentally, the image of an object is calculated by the difference in the transcutaneous voltage between two scenes (i.e. the fish and all the objects in the environment) in which the only difference is the presence or absence of that object. But usually the fish is moving in a complex medium with several objects of interest.
We argue in this paper that when including several objects, the resulting image of the scene is not the addition of the images of the individual objects: by the contrary, the presence of an object distorts the image of others, if close enough. As a consequence, when we determine the image of an object as the difference between the current densities in the presence and absence of the object, the result depends on the context; i.e. the presence and characteristics of other objects and the active changes of the field direction related to the orientation of the skin.
Section snippets
The model
We developed a program to determine the electric image in weakly electric fish using the Boundary Element Method (BEM; Hunter and Pullan, 2001) as proposed by Assad (1997). The program allows the determination of the electrosensory images of weakly electric fish in a given environment (scene). It allows to model fishes of different species, placed in specific positions in an environment with objects, and calculates the currents through the skin. The program, including the user’s manual is
Theoretical considerations
When an object is placed in a basal electric field (F0), it distorts the field in a way (Fd) that depends on the object (O), the basal field (F0) and the other elements in the environment (E). The resulting field is then F=F0+Fd(F0,E,O). Only in very special cases (as for example a sphere in a uniform field) the perturbation of the field is the same as that produced by a dipole (Rasnow, 1996). When the object is far from the fish, the resulting field perturbation is similar to that produced by
Discussion
Since Aristotle, it is accepted that perception is the process that, using the information provided by the senses, constructs mental representations of the world around us (scenes). Since shaped by evolution, perception has to produce a useful representation of reality from the sensory input. It must extract interesting particularities of the environment from a given image (the representation of a scene at the receptor level): e.g. the distance or shape of an object. Vision is the paradigmatic
Acknowledgements
This paper was partially financed by grants from CSIC (Universidad de la República, Uruguay), NIH (USA) and ECOS (French Government and Universidad de la República, Uruguay) and supported by INTAS grant 01-2061.
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