DocumentCode
565202
Title
Design tools for artificial nervous systems
Author
Scheffer, Louis K.
Author_Institution
Howard Hughes Med. Inst., Howard, WI, USA
fYear
2012
fDate
3-7 June 2012
Firstpage
717
Lastpage
722
Abstract
Electronic and biological systems both perform complex information processing, but they use very different techniques. Though electronics has the advantage in raw speed, biological systems have the edge in many other areas. They can be produced, and indeed self-reproduce, without expensive and finicky factories. They are tolerant of manufacturing defects, and learn and adapt for better performance. In many cases they can self-repair damage. These advantages suggest that biological systems might be useful in a wide variety of tasks involving information processing. So far, all attempts to use the nervous system of a living organism for information processing have involved selective breeding of existing organisms. This approach, largely independent of the details of internal operation, is used since we do not yet understand how neural systems work, nor exactly how they are constructed. However, as our knowledge increases, the day will come when we can envision useful nervous systems and design them based upon what we want them to do, as opposed to variations on what has been already built. We will then need tools, corresponding to our Electronic Design Automation tools, to help with the design. This paper is concerned with what such tools might look like.
Keywords
artificial intelligence; bioinformatics; electronic design automation; neurophysiology; artificial nervous systems; biological systems; complex information processing; electronic design automation tools; electronic systems; internal operation details; living organism; manufacturing defects; neural systems; selective organisms breeding; self-repair damage; Animals; Chemicals; Logic gates; Neurons; Biological systems; Design Automation; Neurons;
fLanguage
English
Publisher
ieee
Conference_Titel
Design Automation Conference (DAC), 2012 49th ACM/EDAC/IEEE
Conference_Location
San Francisco, CA
ISSN
0738-100X
Print_ISBN
978-1-4503-1199-1
Type
conf
Filename
6241584
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