The CNN model is now a paradigm of cellular analog programmable multidimensional processor array with distributed local logic and memory. It has turned out that for biological sensory information processing this model is so natural that a lot of neuroanatomical structures can be directly translated into CNN models. In this paper first we show the equivalent notions of neuroanatomy and the CNN model, motivated by studying the visual system. Next, various, mainly subcortical phenomena are studied. Simple effects like directional sensitivity and length tuning are modeled. A more accurate retina model has been developed taking into account some effects of amacrine cells. It is shown that the standard errors occurring in simple models of retinal illusions can be eliminated by using our more accurate models including delays. LGN effects with and without cortical feedback are modeled as well. Their CNN models are simple. Furthermore, simple texture detection effects and motion illusions are explained by neuromorphic CNN models. Local connectivity within a finite neighborhood, equivalent templates of different receptive field organizations, delay-templates have proved to be key issues. The goal is to translate known effects into CNN models and to show a framework for further studies.
|Number of pages||14|
|Journal||IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications|
|Publication status||Published - Mar 1993|
ASJC Scopus subject areas
- Electrical and Electronic Engineering