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Neuromorphic electronic engineering takes its inspiration from the functioning of nervous systems to build more power efficient electronic sensors and processors. Event-based neuromorphic systems are inspired by the brain's efficient data-driven communication design, which is key to its quick responses and remarkable capabilities. This cross-disciplinary text establishes how circuit building blocks are combined in architectures to construct complete systems. These include vision and auditory sensors as well as neuronal processing and learning circuits that implement models of nervous systems. Techniques for building multi-chip scalable systems are considered throughout the book, including me...
An introduction to the design of analog VLSI circuits. Neuromorphic engineers work to improve the performance of artificial systems through the development of chips and systems that process information collectively using primarily analog circuits. This book presents the central concepts required for the creative and successful design of analog VLSI circuits. The discussion is weighted toward novel circuits that emulate natural signal processing. Unlike most circuits in commercial or industrial applications, these circuits operate mainly in the subthreshold or weak inversion region. Moreover, their functionality is not limited to linear operations, but also encompasses many interesting nonlinear operations similar to those occurring in natural systems. Topics include device physics, linear and nonlinear circuit forms, translinear circuits, photodetectors, floating-gate devices, noise analysis, and process technology.
Based upon the most advanced human-made technology on this planet, CMOS integrated circuit technology, this dissertation examines the design of hardware components and systems to establish a technological foundation for the application of future breakthroughs in the intersection of AI and neuroscience. Humans have long imagined machines, robots, and computers that learn and display intelligence akin to animals and themselves. To advance the development of these machines, specialised research in custom-built hardware designed for specific types of computation, which mirrors the structure of powerful biological nervous systems, is especially important. This dissertation is driven by the quest ...
Biological sensory systems, fine-tuned to their specific tasks with remarkable perfection, have an enormous potential for technical, industrial, and medical applications. This applies to sensors specialized for a wide range of energy forms such as optical, mechanical, electrical, and magnetic, to name just a few. This book brings together first-hand knowledge from the frontiers of different fields of research in sensing. It aims to promote the interaction between biologists, engineers, physicists, and mathematicians and to pave the way for innovative lines of research and cross-disciplinary approaches. The topics presented cover a broad spectrum ranging from energy transformation and transduction processes in animal sensing systems to the fabrication and application of bio-inspired synthetic sensor arrays. The various contributions are linked by the similarity of what sensing has to accomplish in both biology and engineering.
Machine learning, and specifically deep learning, has been hugely disruptive in many fields of computer science. The success of deep learning techniques in solving notoriously difficult classification and regression problems has resulted in their rapid adoption in solving real-world problems. The emergence of deep learning is widely attributed to a virtuous cycle whereby fundamental advancements in training deeper models were enabled by the availability of massive datasets and high-performance computer hardware. This text serves as a primer for computer architects in a new and rapidly evolving field. We review how machine learning has evolved since its inception in the 1960s and track the ke...
Both the visualisation and management of large-scale computer hardware is difficult due to its distributed nature. This thesis develops a framework to support both these goals on the SpiNNaker neural network architecture - which can scale to more than a million processors. The solution provides visualisation and management to the SpiNNaker machine, traversing the hardware and software divide to provide a unified solution for the real-time monitoring of artificial neural networks, and the SpiNNaker hardware on which it runs. This book is the story of its development
Theoretical neuroscience provides a quantitative basis for describing what nervous systems do, determining how they function, and uncovering the general principles by which they operate. This text introduces the basic mathematical and computational methods of theoretical neuroscience and presents applications in a variety of areas including vision, sensory-motor integration, development, learning, and memory. The book is divided into three parts. Part I discusses the relationship between sensory stimuli and neural responses, focusing on the representation of information by the spiking activity of neurons. Part II discusses the modeling of neurons and neural circuits on the basis of cellular and synaptic biophysics. Part III analyzes the role of plasticity in development and learning. An appendix covers the mathematical methods used, and exercises are available on the book's Web site.
Proceedings of the 2002 Neural Information Processing Systems Conference.
This chapter presents a set of introductory material, which in addition to providing a general view on the topic, highlights the importance of research in this area. It also presents a short history of the design of smart vision sensors, and points out some of the fundamental issues in the design of such sensors. 1. 1 A General Overview Machine vision is one of the main branches of artificial intelligence. The richness of information present in images makes them the first choice as an input to an artificial system which tries to interact with its environment. A large proportion of the brain of many advanced species is dedicated to visual information processing, which illustrates the importan...
The iPad. The Kindle. Twitter. When the Best Technology Writing series was inaugurated in 2005, these technologies did not exist. Now they define our 21st-century lives. As Julian Dibbell writes in his introduction to "The Best Technology Writing 2010, ""The digital is us. Yet for that reason, it is also something more, a lightning rod for our feelings about technology in general." Whether it is Sam Anderson's giddy but troubled defense of online distractions, David Carr's full-throated elegy to the dying world of pre-digital publishing, Steven Johnson's warm appreciation of Twitter's bite-size contributions to collective human intelligence, or Evan Ratliff's fascinating month-long quest to ...