Article for analog vector algebra computation

We introduce the concept of an analog neural network represented by chemica l operations performed on strands of DNA. This new type of DNA computing ha s the advantage that it should be fault tolerant and thus more immune to DN A hybridization errors than a Boolean DNA computer. We describe a particula r 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 a s many as 10(9) neurons might be feasible. (C) 1999 Elsevier Science Irelan d Ltd. All rights reserved.