A Silicon Model of Early Visual Processing

Many of the most striking phenomena known from perceptual psychology are a direct result of the first levels of neural processing. In the visual systems of higher animals, the well-known center-surround response to local stimuli is responsible for some of the strongest visual illusions. For example, Mach bands, the Hermann-Hering grid illusion, and the Craik-O'Brian-Comsweet illusion can all be traced to simple inhibitory interactions between elements of the retina (Ratliff 1965). The high degree to which a perceived image is independent of the absolute illumination level can be viewed as a property of the mechanism by which incident light is transduced into an electrical signal. We present a model of the first stages of retinal processing in which these phenomena are viewed as natural by-products of the mechanism by which the system adapts to a wide range of viewing conditions. Our retinal model is implemented as a single silicon chip, which contains integrated photoreceptors and processing elements; this chip generates, in real time, outputs that correspond directly to signals observed in the corresponding levels of biological retinas

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

Event-based Vision: A Survey

Event cameras are bio-inspired sensors that differ from conventional frame cameras: Instead of capturing images at a fixed rate, they asynchronously measure per-pixel brightness changes, and output a stream of events that encode the time, location and sign of the brightness changes. Event cameras offer attractive properties compared to traditional cameras: high temporal resolution (in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low power consumption, and high pixel bandwidth (on the order of kHz) resulting in reduced motion blur. Hence, event cameras have a large potential for robotics and computer vision in challenging scenarios for traditional cameras, such as low-latency, high speed, and high dynamic range. However, novel methods are required to process the unconventional output of these sensors in order to unlock their potential. This paper provides a comprehensive overview of the emerging field of event-based vision, with a focus on the applications and the algorithms developed to unlock the outstanding properties of event cameras. We present event cameras from their working principle, the actual sensors that are available and the tasks that they have been used for, from low-level vision (feature detection and tracking, optic flow, etc.) to high-level vision (reconstruction, segmentation, recognition). We also discuss the techniques developed to process events, including learning-based techniques, as well as specialized processors for these novel sensors, such as spiking neural networks. Additionally, we highlight the challenges that remain to be tackled and the opportunities that lie ahead in the search for a more efficient, bio-inspired way for machines to perceive and interact with the world

Integrated 2-D Optical Flow Sensor

I present a new focal-plane analog VLSI sensor that estimates optical flow in two visual dimensions. The chip significantly improves previous approaches both with respect to the applied model of optical flow estimation as well as the actual hardware implementation. Its distributed computational architecture consists of an array of locally connected motion units that collectively solve for the unique optimal optical flow estimate. The novel gradient-based motion model assumes visual motion to be translational, smooth and biased. The model guarantees that the estimation problem is computationally well-posed regardless of the visual input. Model parameters can be globally adjusted, leading to a rich output behavior. Varying the smoothness strength, for example, can provide a continuous spectrum of motion estimates, ranging from normal to global optical flow. Unlike approaches that rely on the explicit matching of brightness edges in space or time, the applied gradient-based model assures spatiotemporal continuity on visual information. The non-linear coupling of the individual motion units improves the resulting optical flow estimate because it reduces spatial smoothing across large velocity differences. Extended measurements of a 30x30 array prototype sensor under real-world conditions demonstrate the validity of the model and the robustness and functionality of the implementation

Scoping Study for a Realistic Driving Simulator: Final Report.

1. INTRODUCTION This report documents the results of a study carried out between December 1989 and March 1990 to determine the most suitable equipment to be purchased for building a driving simulator at the Institute for Transport Studies at the University of Leeds. This "scoping study" was intended to accomplish three main tasks: 1. A review of existing facilities both in the UK and elsewhere in Europe to ascertain what has already been achieved and what is the current state of the art. 2. Initial discussions with potential users on desired features to be built in to the simulator. 3. Discussions with equipment suppliers in the light of what was found out in Tasks 1 and 2, so that the appropriate equipment could be specified. The report documents in subsequent sections the findings of the first two tasks. It then summarizes the conclusions that were reached on the overall simulator design, on the required features of the simulator and on the effort required to develop an operational simulator from the various hardware components. Finally, recommendations are made on the equipment to be purchased in the light of the recommended configuration, the discussion with equipment suppliers under Task 3 and the budget allocated

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system