Reusable Noncomplementary DNA-Based Neural Network

Neural network computation is a cornerstone of modern artificial intelligence, with electronic software-based approaches achieving widespread success due to their ability to enable continuous, iterative learning on the same platform. DNA-based neural networks, with their potential advantages in vers...

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Bibliographic Details
Main Author:	Chengjie Sun (1449106) (author)
Other Authors:	Xiaoyang Liu (1815211) (author), Jiafeng Zhong (13026246) (author), Qin Zhou (239082) (author), Jianjun Cheng (646595) (author)
Published:	2025
Subjects:	Biophysics Biotechnology Computational Biology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified work pioneers reusability weighting values modulated raises operational costs modern artificial intelligence first successful demonstration despite significant advancements critical reusability challenge based neural networks based neural network four input strands implementing learning mechanisms unprecedented noncomplementary dna noncomplementary dna duplexes molecular computing systems noncomplementary “ winner reusable noncomplementary dna noncomplementary dna reusable dna based perceptron traditional systems four pairs computing materials learning capabilities iterative learning dna nucleobases current dna simply achieved promising alternative practical path potential advantages pattern recognition energy efficiency enable continuous electronic software duplex concentrations displacement reactions computation strategy complementary pairing also restricts algorithm accuracy
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Reusable Noncomplementary DNA-Based Neural Network

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