Abstract

Most current models of human and animal vision assume that the processes of vision involve 2D (or even 3D) internal representations of the external world--an iconic representation. Within these models, recognition involves some form of lineal, areal or volumetric comparison of these internal representations (either learned or innate) with current sensory inputs. However, this view has recently come under criticism. In this paper, a neural model of vision is explored in which this iconic world view is replaced by a temporally ordered trace of (essentially) local features. The model employs hierarchical, recurrently linked, self-organizing topological maps.

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