Ultradiffusion, scale space transformation, and the morphology of neural networks

The author proposes the scale-space transformation (SST) as a paradigm for information processing in biological neural networks. The SST concept includes scale-space, scale-time, and scale-space-time mappings. Hierarchical nonlinear (HNL) systems theory, together with the SST paradigm, causality requirements in the time domain, and uncertainty constraints in time and space domains, can be used to develop morphogenic models of biological neural networks. Since morphogenic models need only capture the functional modality of their physical counterparts, there may or may not be an observable resemblance to physical structure. To illustrate these concepts, the author discusses a morphogenic model of the mammalian visual system (MVS) in terms of SST mappings. As an example he uses an exponential retinotopic mapping, which is called the log Z SST (LZ SST). Using HNL and SST concepts, the author suggests a layered model of the MVS neural network.<>