Abstract
The target localization system of the barn owl adaptively fuses its separate visual and auditory representations of object position into a single joint representation used for head orientation. The building block in this system is the neuronal map. Neuronal maps are large arrays of locally interconnected neurons that represent information in a map-like form, that is, the value of stimulus parameters is encoded by the position of neural activation in the array. The computational load is distributed to a hierarchy of maps, and computation is performed in stages by transforming the representation from map to map via the geometry of the projections between the maps and the local interactions within the maps. In the owl one sequence of maps is used to compute azimuth from binaural phase difference, while another sequence is used to derive elevation from binaural intensity difference. These separate streams are recombined to produce a map of space. In doing this, the owl’s nervous system must deal with at least two kinds of ghosts that are eliminated before the visual and auditory maps of space are fused. We present models for the elimination of these ghosts. These models suggest mechanisms for the adaptive registration of the visual and auditory inputs to the fused map of space in the optic tectum.
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© 1993 Springer-Verlag Berlin Heidelberg
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Spence, C.D., Pearson, J.C. (1993). Adaptive Visual/Auditory Fusion in the Target Localization System of the Barn Owl. In: Aggarwal, J.K. (eds) Multisensor Fusion for Computer Vision. NATO ASI Series, vol 99. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-02957-2_25
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DOI: https://doi.org/10.1007/978-3-662-02957-2_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-08135-4
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