Programmable retinal dynamics in a CMOS mixed-signal array processor chip

The low-level image processing that takes place in the retina is intended to compress the relevant visual information to a manageable size. The behavior of the external layers of the biological retina has been successfully modelled by a Cellular Neural Network, whose evolution can be described by a set of coupled nonlinear differential equations. A mixed-signal VLSI implementation of the focal-plane low-level image processing based upon this biological model constitutes a feasible and cost effective alternative to conventional digital processing in real-time applications. For these reasons, a programmable array processor prototype chip has been designed and fabricated in a standard 0.5μm CMOS technology. The integrated system consists of a network of two coupled layers, containing 32 × 32 elementary processors, running at different time constants. Involved image processing algorithms can be programmed on this chip by tuning the appropriate interconnections weights. Propagative, active wave phenomena and retina-like effects can be observed in this chip. Design challenges, trade-offs, the buildings blocks and some test results are presented in this paper.Office of Naval Research (USA) N00014-00-10429European Community IST-1999-19007Ministerio de Ciencia y Tecnología TIC1999-082

Neuromorphic analogue VLSI

Neuromorphic systems emulate the organization and function of nervous systems. They are usually composed of analogue electronic circuits that are fabricated in the complementary metal-oxide-semiconductor (CMOS) medium using very large-scale integration (VLSI) technology. However, these neuromorphic systems are not another kind of digital computer in which abstract neural networks are simulated symbolically in terms of their mathematical behavior. Instead, they directly embody, in the physics of their CMOS circuits, analogues of the physical processes that underlie the computations of neural systems. The significance of neuromorphic systems is that they offer a method of exploring neural computation in a medium whose physical behavior is analogous to that of biological nervous systems and that operates in real time irrespective of size. The implications of this approach are both scientific and practical. The study of neuromorphic systems provides a bridge between levels of understanding. For example, it provides a link between the physical processes of neurons and their computational significance. In addition, the synthesis of neuromorphic systems transposes our knowledge of neuroscience into practical devices that can interact directly with the real world in the same way that biological nervous systems do

Silicon retina with adaptive photoreceptors

The central problem faced by the retina is to encode reliably small local differences in image intensity over a several-decade range of background illumination. The distal layers of the retina adjust the transducing elements to make this encoding possible. Several generations of silicon retinae that integrate phototransducers and CMOS processing elements in the focal plane are modeled after the distal layers of the vertebrate retina. A silicon retina with an adaptive photoreceptor that responds with high gain to small spatial and temporal variations in light intensity is described. Comparison with a spatial and temporal average of receptor response extends the dynamic range of the receptor. Continuous, slow adaptation centers the operating point of the photoreceptor around its time-average intensity and compensates for static transistor mismatch

High dynamic range perception with spatially variant exposure

In this paper we present a method capable of perceiving high dynamic range scene. The special feature of the method is that it changes the integration time of the imager on the pixel level. Using CNN-UM we can calculate the integration time for the pixels, and hence low dynamic range integration type CMOS sensors will be able to perceive high dynamic range scenes. The method yields high contrast without introducing non-existing edges

A Biomimetic Model of the Outer Plexiform Layer by Incorporating Memristive Devices

In this paper we present a biorealistic model for the first part of the early vision processing by incorporating memristive nanodevices. The architecture of the proposed network is based on the organisation and functioning of the outer plexiform layer (OPL) in the vertebrate retina. We demonstrate that memristive devices are indeed a valuable building block for neuromorphic architectures, as their highly non-linear and adaptive response could be exploited for establishing ultra-dense networks with similar dynamics to their biological counterparts. We particularly show that hexagonal memristive grids can be employed for faithfully emulating the smoothing-effect occurring at the OPL for enhancing the dynamic range of the system. In addition, we employ a memristor-based thresholding scheme for detecting the edges of grayscale images, while the proposed system is also evaluated for its adaptation and fault tolerance capacity against different light or noise conditions as well as distinct device yields

VLSI analogs of neuronal visual processing: a synthesis of form and function

This thesis describes the development and testing of a simple visual system fabricated using complementary metal-oxide-semiconductor (CMOS) very large scale integration (VLSI) technology. This visual system is composed of three subsystems. A silicon retina, fabricated on a single chip, transduces light and performs signal processing in a manner similar to a simple vertebrate retina. A stereocorrespondence chip uses bilateral retinal input to estimate the location of objects in depth. A silicon optic nerve allows communication between chips by a method that preserves the idiom of action potential transmission in the nervous system. Each of these subsystems illuminates various aspects of the relationship between VLSI analogs and their neurobiological counterparts. The overall synthetic visual system demonstrates that analog VLSI can capture a significant portion of the function of neural structures at a systems level, and concomitantly, that incorporating neural architectures leads to new engineering approaches to computation in VLSI. The relationship between neural systems and VLSI is rooted in the shared limitations imposed by computing in similar physical media. The systems discussed in this text support the belief that the physical limitations imposed by the computational medium significantly affect the evolving algorithm. Since circuits are essentially physical structures, I advocate the use of analog VLSI as powerful medium of abstraction, suitable for understanding and expressing the function of real neural systems. The working chip elevates the circuit description to a kind of synthetic formalism. The behaving physical circuit provides a formal test of theories of function that can be expressed in the language of circuits

Biosensor development at the University of Utah

Journal ArticleInterest in biosensors has increased rapidly in the past few years due to the many potential advantages of these devices, such as small size, speed of response, and specificity 111. The term "biosensor" in the broad sense describes any device or apparatus which detects biological signals for the purpose of diagnosis, monitoring, imaging or sensing the state of the biological organism. This includes the more narrow definition-that of a biosensor as a continuous, reversible monitor of some physiological parameter

Implementing neural architectures using analog VLSI circuits

Analog very large-scale integrated (VLSI) technology can be used not only to study and simulate biological systems, but also to emulate them in designing artificial sensory systems. A methodology for building these systems in CMOS VLSI technology has been developed using analog micropower circuit elements that can be hierarchically combined. Using this methodology, experimental VLSI chips of visual and motor subsystems have been designed and fabricated. These chips exhibit behavior similar to that of biological systems, and perform computations useful for artificial sensory systems

Bacteriorhodopsin and its Mutants allude a breakthrough impending to artificial retina construction and strategies for curing blindness

       Bacteriorhodopsin, a model system in nanobiotechnology, is a light-sensitive protein found in the archaean Halobacterium salinarum and a very identical protein to visual Rhodopsin. The modification of biological function of BR and its versatile properties is valuable for technical applications including the artificial retina. These photoactive elements of native and particular mutants of bacteriorhodopsin make protein films, used in artificial retinal implants, to treat some retinal diseases and disorders. The two major reasons of retinal photoreceptor cell deterioration are Age-related Macular Degeneration (AMD) and Retinitis Pigmentosa (RP). As in vitro culture of Halobacterium is very difficult, and isolation procedure is much time consuming and usually inefficient, so genetic construction of protein is essential. Here, we have produced two types of bacteriorhodopsin, a native and a mutant BR (D85E) and studied their opto-electric responses with respect to wavelength and absorption properties. They are prerequisite for designing artificial retina (sensors) based on biomolecules. Therefore, the new promising technology soon will conceivably eradicate the blindness