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Brain‐inspired computing.

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Title: Brain‐inspired computing.
Authors: Furber, Steve B.1 (AUTHOR) steve.furber@manchester.ac.uk
Source: IET Computers & Digital Techniques (Wiley-Blackwell). Nov2016, Vol. 10 Issue 6, p299-305. 7p.
Abstract: The inner workings of the brain as a biological information processing system remain largely a mystery to science. Yet there is a growing interest in applying what is known about the brain to the design of novel computing systems, in part to explore hypotheses of brain function, but also to see if brain‐inspired approaches can point to novel computational systems capable of circumventing the limitations of conventional approaches, particularly in the light of the slowing of the historical exponential progress resulting from Moore's Law. Although there are, as yet, few compelling demonstrations of the advantages of such approaches in engineered systems, a number of large‐scale platforms have been developed recently that promise to accelerate progress both in understanding the biology and in supporting engineering applications. SpiNNaker (Spiking Neural Network Architecture) is one such large‐scale example, and much has been learnt in the design, development and commissioning of this machine that will inform future developments in this area. [ABSTRACT FROM AUTHOR]
Copyright of IET Computers & Digital Techniques (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: The inner workings of the brain as a biological information processing system remain largely a mystery to science. Yet there is a growing interest in applying what is known about the brain to the design of novel computing systems, in part to explore hypotheses of brain function, but also to see if brain‐inspired approaches can point to novel computational systems capable of circumventing the limitations of conventional approaches, particularly in the light of the slowing of the historical exponential progress resulting from Moore's Law. Although there are, as yet, few compelling demonstrations of the advantages of such approaches in engineered systems, a number of large‐scale platforms have been developed recently that promise to accelerate progress both in understanding the biology and in supporting engineering applications. SpiNNaker (Spiking Neural Network Architecture) is one such large‐scale example, and much has been learnt in the design, development and commissioning of this machine that will inform future developments in this area. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IET Computers & Digital Techniques (Wiley-Blackwell) is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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