Article for analog vector algebra computation

by: AP Mills, B Yurke, PM Platzman

Biosystems, Vol. 52, No. 1-3. (October 1999), pp. 175-180.

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Abstract

We introduce the concept of an analog neural network represented by chemical operations performed on strands of DNA. This new type of DNA computing has the advantage that it should be fault tolerant and thus more immune to DNA hybridization errors than a Boolean DNA computer. We describe a particular set of DNA operations to effect the interconversion of electrical and DNA data and to represent the Hopfield associative memory and the feed-forward neural network of Rumelhart et al. We speculate that networks containing as many as 10(9) neurons might be feasible.

@article{Mills99, abstract = {We introduce the concept of an analog neural network represented by chemical operations performed on strands of DNA. This new type of DNA computing has the advantage that it should be fault tolerant and thus more immune to DNA hybridization errors than a Boolean DNA computer. We describe a particular set of DNA operations to effect the interconversion of electrical and DNA data and to represent the Hopfield associative memory and the feed-forward neural network of Rumelhart et al. We speculate that networks containing as many as 10(9) neurons might be feasible.}, address = {Bell Laboratories, Lucent Technologies, Murray Hill, NJ 07974, USA. apm@physics.lucent.com}, author = {Mills, A. P. and Yurke, B. and Platzman, P. M. }, citeulike-article-id = {532053}, issn = {0303-2647}, journal = {Biosystems}, keywords = {dna\_computing}, month = {October}, number = {1-3}, pages = {175--180}, posted-at = {2006-03-06 23:53:55}, priority = {2}, title = {Article for analog vector algebra computation}, url = {http://view.ncbi.nlm.nih.gov/pubmed/10636042}, volume = {52}, year = {1999} }

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TY - JOUR ID - Mills99 L3 - citeulike-article-id:532053 TI - Article for analog vector algebra computation JF - Biosystems VL - 52 IS - 1-3 SP - 175 EP - 180 AD - Bell Laboratories, Lucent Technologies, Murray Hill, NJ 07974, USA. apm@physics.lucent.com SN - 0303-2647 N2 - We introduce the concept of an analog neural network represented by chemical operations performed on strands of DNA. This new type of DNA computing has the advantage that it should be fault tolerant and thus more immune to DNA hybridization errors than a Boolean DNA computer. We describe a particular set of DNA operations to effect the interconversion of electrical and DNA data and to represent the Hopfield associative memory and the feed-forward neural network of Rumelhart et al. We speculate that networks containing as many as 10(9) neurons might be feasible. KW - dna_computing AU - Mills, AP AU - Yurke, B AU - Platzman, PM PY - 1999/10// UR - http://view.ncbi.nlm.nih.gov/pubmed/10636042 ER -

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