Design of Multi-Competitors Winner-Take-All Neural Networks Based on DNA Strand Displacement for Molecular Pattern Recognition

Abstract

DNA strand displacement technology (DSDT) provides flexible and powerful technical support for DNA molecular computing. DNA-based neural networks with Winner-Take-All (WTA) strategy has a great potential for nonlinear calculation. However, so far it has been limited to achieving the simultaneous competition of two competitors. Optimizing the calculation model and reducing system response time to recognize complex and functional molecular patterns remains a huge challenge. Here a novel neural network with WTA strategy based on DSDT was constructed, which allowed three competitors to participate in the competition at the same time. Firstly, the feasibility of the three-competitor WTA neural network was proved by 9-bit pattern recognition. Then the three-competitors WTA neural network was further extended to larger scale pattern recognition, which successfully recognized 64-bit letters A, B, and C and 100-bit handwritten digits 0, 2, and 4, respectively. Simulations showed that when recognizing the same target patterns with same number bits, compared with two-competitors WTA neural network, the three-competitors WTA network only used down to two-thirds DNA strands, and the system response time was reduced by more than ten times. This paper demonstrated the efficient recognition ability of the three-competitor WTA neural network, which is expected to be used to identify more complex information.