László Orzó

The use of CNN models in the subcortical visual pathway

The equivalent notions of neuroanatomy and the cellular neural network (CNN) model are discussed with a view toward studying the visual system. Various mainly subcortical phenomena are studied and 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 the more accurate models including delays. Lateral geniculate nucleus (LGN) effects with and without cortical feedback are modeled as well; their CNN models are simple. Simple texture detection effects and motion illusions are explained by neuromorphic CNN models. The goal is to translate known effects into CNN models and to provide a framework for further studies. >

POAC (programmable optical array computer) applied for target recognition and tracking

A portable programmable opto-electronic analogic CNN computer (Laptop-POAC) has been built and used to recognize and track targets. Its kernel processor is a novel type of high performance optical correlator based on the use of bacteriorhodopsin (BR) as a dynamic holographic material. This optical CNN implementation combines the optical computers high speed, high parallelism (≈106 channel) and large applicable template sizes with flexible programmability of the CNN devices. Unique feature of this optical array computer is that programming templates can be applied either by a 2D acousto-optical deflector (up to 64x64 pixel size templates) incoherently or by an LCD-SLM (up to 128x128 size templates) coherently. So it can work both in totally coherent and partially incoherent way, utilizing the actual advantages of the used mode of operation. Input images are fed-in by a second LCD-SLM of 600x800 pixel resolution. Evaluation of trade-off between speed and resolution is given. Novel and effective target recognition and multiple-target-tracking algorithms have been developed for the POAC. Tracking experiments are demonstrated. Collision avoidance experiments are being conducted. In the present model a CCD camera is recording the correlograms, however, later a CNN-UM chip and a high-speed CMOS camera will be applied for post-processing.