Abstract
Neural networks for the processing of sensory information show remarkable similarities between different species and across different sensory modalities. As an example, cortical organization found in the mamalian neopallium and in the optic tecta of most vertebrates appears to be equally appropriate as a substrate for visual, auditory, and somatosensory information processing. In this paper, we formulate three structural principles of the vertebrate visual cortex that allow to analyze structure and function of these neural networks on an intermediate level of complexity. Computational applications are taken from the field of early vision. The proposed principles are: (a) Average anatomy, i e, lamination, average axonal and dendritic domains, and intrinsic feedback, determines the spatiotemporal interactions in cortical processing. Possible applications of the resulting filters include continuous motion perception and the direct measurement of high-level parameters of image flow. (b) Retinotopic mapping is an emergent property of massively parallel connections. With a local intrinsic operation in the target area, mapping combines to a space-variant image processing system as would be useful in the analysis of optical flow. (c) Further space-variance is brought about by both, discrete or patchy connections between areas and periodic or columnar arrangement of specialized neurons within the areas. We present preliminary results on the significance of these principles for neural computation.
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Mallot, H.A., Von Seelen, W. (1989). Why Cortices? Neural Networks for Visual Information Processing. In: Ewert, JP., Arbib, M.A. (eds) Visuomotor Coordination. Springer, Boston, MA. https://doi.org/10.1007/978-1-4899-0897-1_11
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DOI: https://doi.org/10.1007/978-1-4899-0897-1_11
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