BibTeX
@MISC{Smith04implementingneural,
author = {Leslie S. Smith},
title = {Implementing neural models in silicon},
year = {2004}
}
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Abstract
Neural models are used in both computational neuroscience and in pattern recognition. The aim of the first is understanding of real neural systems, and of the second is gaining better, possibly brain-like performance for systems being built. In both cases, the highly parallel nature of the neural system contrasts with the sequential nature of computer systems, resulting in slow and complex simulation software. More direct implementation in hardware (whether digital or analogue) holds out the promise of faster emulation both because hardware implementation is inherently faster than software, and because the operation is much more parallel. There are costs to this: modifying the system (for example to test out variants of the system) is much harder when a full application specific integrated circuit has been built. Fast emulation can permit direct incorporation of a neural model into a system, permitting realtime input and output. Appropriate selection of implementation technology can help to make interfacing the system to external devices simpler. We review the technologies involved, and discuss some example systems. 1 Why implement neural models in silicon? There are two primary reasons for implementing neural models: one is to attempt to gain better, and possibly
Keyphrases
neural model direct incorporation complex simulation software hardware implementation implement neural model external device simpler appropriate selection implementation technology computer system parallel nature sequential nature brain-like performance full application primary reason neural system fast emulation realtime input pattern recognition real neural system computational neuroscience direct implementation much harder example system
