CARVER MEAD NEUROMORPHIC ELECTRONIC SYSTEMS PDF

Biological in forma tion-processing systems operate on completely different principles from those with which most engineers are familiar. For many problems, particularly those in which the input data are ill-conditioned and the computation can be specified in a relative manner, biological solutions are many orders of magnitude more effective than those we have been able to implement using digital methods. This advantage can be attributed principally to the use of elementary physical phenomena as computational primitives, and to the representation of information by the relative values of analog signals, rather than by the absolute values of digital signals. This approach requires adaptive techniques to mitigate the effects of component differences. This kind of adaptation leads naturally to systems that learn about their environment. Large-scale adaptive analog systems are more robust to component degredation and failure than are more conventional.

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Mead, Carver Neuromorphic electronic systems. Proceedings of the IEEE, 78 ISSN Biological in formation-processing systems operate on completely different principles from those with which most engineers are familiar. For many problems, particularly those in which the input data are ill-conditioned and the computation can be specified in a relative manner, biological solutions are many orders of magnitude more effective than those we have been able to implement using digital methods.

This advantage can be attributed principally to the use of elementary physical phenomena as computational primitives, and to the representation of information by the relative values of analog signals, rather than by the absolute values of digital signals. This approach requires adaptive techniques to mitigate the effects of component differences. This kind of adaptation leads naturally to systems that learn about their environment.

Large-scale adaptive analog systems are more robust to component degradation and failure than are more conventional systems, and they use far less power. For this reason, adaptive analog technology can be expected to utilize the full potential of wafer-scale silicon fabrication. Repository Staff Only: item control page. A Caltech Library Service. Neuromorphic electronic systems. Abstract Biological in formation-processing systems operate on completely different principles from those with which most engineers are familiar.

More information and software credits. Neuromorphic electronic systems Mead, Carver Neuromorphic electronic systems. Manuscript received February 1, ; revised March 23, No commercial reproduction, distribution, display or performance rights in this work are provided. Kristin Buxton.

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Neuromorphic engineering

Mead, Carver Neuromorphic electronic systems. Proceedings of the IEEE, 78 ISSN Biological in formation-processing systems operate on completely different principles from those with which most engineers are familiar. For many problems, particularly those in which the input data are ill-conditioned and the computation can be specified in a relative manner, biological solutions are many orders of magnitude more effective than those we have been able to implement using digital methods.

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Carver Mead

Neuromorphic engineering , also known as neuromorphic computing , [1] [2] [3] is a concept developed by Carver Mead , [4] in the late s, describing the use of very-large-scale integration VLSI systems containing electronic analog circuits to mimic neuro-biological architectures present in the nervous system. The implementation of neuromorphic computing on the hardware level can be realized by oxide-based memristors , [6] spintronic memories, [7] threshold switches, and transistors. A key aspect of neuromorphic engineering is understanding how the morphology of individual neurons, circuits, applications, and overall architectures creates desirable computations, affects how information is represented, influences robustness to damage, incorporates learning and development, adapts to local change plasticity , and facilitates evolutionary change. Neuromorphic engineering is an interdisciplinary subject that takes inspiration from biology , physics , mathematics , computer science , and electronic engineering to design artificial neural systems, such as vision systems , head-eye systems, auditory processors, and autonomous robots, whose physical architecture and design principles are based on those of biological nervous systems. As early as , researchers at Georgia Tech published a field programmable neural array. In November , a group of MIT researchers created a computer chip that mimics the analog, ion-based communication in a synapse between two neurons using transistors and standard CMOS manufacturing techniques.

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Computing Technology Overview

Conventional vision, auditory, and olfactory sensors generate large volumes of redundant data and as a result tend to consume excessive power. To address these shortcomings, neuromorphic sensors have been developed. These sensors mimic the neuro-biological architecture of sensory organs using aVLSI analog Very Large Scale Integration and generate asynchronous spiking output that represents sensing information in ways that are similar to neural signals. This allows for much lower power consumption due to an ability to extract useful sensory information from sparse captured data.

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Neuromorphic electronic systems

Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. DOI: Mead Published Computer Science. It is shown that for many problems, particularly those in which the input data are ill-conditioned and the computation can be specified in a relative manner, biological solutions are many orders of magnitude more effective than those using digital methods.

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