A bio-inspired model for an analog programmable array processor (APAP), based on studies on the vertebrate retina, has permitted the realization of complex programmable spatio-temporal dynamics in VLSI. This model mimics the way in which images are processed in the visual pathway, what renders a feasible alternative for the implementation of early vision tasks in standard technologies. A prototype chip has been designed and fabricated in 0.5 /spl mu/m CMOS. It renders a computing power per silicon area and power consumption that is amongst the highest reported for a single chip. The details of the bio-inspired network model, the analog building block design challenges and trade-offs and some functional tests results are presented in this paper.

There is hardly a more important sensory modality for humans and other mammals than vision. The first and best-known part of the visual system is the retina, which is not a mere photoreceptor or static camera but a sophisticated feature preprocessor with continuous input and several parallel output channels. These interacting channels represent the visual scene. Never before has it been known in neuroscience how these channels build up a "visual language". Our mammalian retina model can generate the elements of this visual language. In the present paper the design steps of the implementation of the multilayer CNN retinal model is shown. It is rare that an analogic CNN algorithm has such a sophisticated series of different complex dynamics, meanwhile it is feasible on a recently fabricated complex cell CNN-UM chip. The mammalian retina model is decomposed into a full-custom mixed-signal chip that embeds digitally programmable analog parallel processing and distributed image memory on a common silicon substrate. The chip was designed and manufactured in a standard 0.5 μm CMOS technology and contains approximately 500,000 transistors. It consists of 1024 processing units arranged into a 32×32 grid. The functional features of the chip are in accordance with the second-order complex cell CNN-UM architecture: two CNN layers with programmable inter- and intra-layer connections between cells as well as programmable layer time constants. The uniqueness of this approach, among others, lies in the reprogrammability, i.e. the openness to any new discovery, even after a possible retinal implementation.

Abstract. The effect of boundary conditions on the global dynamics of cellular neural networks (CNNs) is investigated. As a case study one-dimensional template CNNs are considered. It is shown that if the off-diagonal template elements have opposite sign, then the boundary conditions behave as bifurcation parameters and can give rise to a very rich and complex dynamic behavior. In particular they determine the equilibrium point patterns, the transition from stability to instability and the occurrence of several bifurcation phenomena leading to strange and/or chaotic attractors and to the coexistence of several attractors. Then the influence of the number of cells on the global dynamics is studied, with particular reference to the occurrence of hyperchaotic behavior.