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Volume 43 Issue 8
Aug.  2021
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Bin WANG, Ya LI, Hongwei ZHAO. The Winner-Take-All Neural Network Based on DNA Strand Displacement[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2430-2438. doi: 10.11999/JEIT200579
Citation: Bin WANG, Ya LI, Hongwei ZHAO. The Winner-Take-All Neural Network Based on DNA Strand Displacement[J]. Journal of Electronics & Information Technology, 2021, 43(8): 2430-2438. doi: 10.11999/JEIT200579

The Winner-Take-All Neural Network Based on DNA Strand Displacement

doi: 10.11999/JEIT200579
Funds:  The National Key R&D Program of China (2018YFC0910500), The National Natural Science Foundation of China (61425002, 61751203, 61772100, 61972266, 61802040, 61672121), The Natural Science Foundation of Liaoning of Province (20180551241, 2019-ZD-0567)
  • Received Date: 2020-07-15
  • Rev Recd Date: 2020-11-10
  • Available Online: 2020-11-23
  • Publish Date: 2021-08-10
  • DNA strand displacement technology is widely used in biological computing, and it has excellent performance in computing power and information processing. However, the use of DNA Strand Displacement (DSD) technology in some calculations, such as signal amplification, restoration, and comparison, not only increases the number of DNA strands, but also brings additional calculation costs. Therefore, in order to reduce the number of DNA strands used, a Winner-Take-All (WTA) neural network based on DNA strand displacement is constructed. Firstly, the logic operations AND, NAND, and OR are realized through neurons, and the linear inseparable problem is solved by cascading them into a WTA neural network. By comparing with the results with others, the effectiveness of the method is proved, and stable and intuitive results are obtained in Visual DSD (DNA Strand Displacement). Then, in order to test the scalability of the neuron cascade, a three-person voter is designed and the scientists are classified. The paper shows how the molecular system demonstrates the ability to think in a similar way to the brain, and finally proves the accuracy is higher than other methods.
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