Deep convolutional and fully-connected DNA neural networks.

Abstract

DNA molecules can be used to build "neural networks" that function like the brain, enabling them to perform complex computational tasks. However, a fundamental limitation of existing DNA networks is that their most basic computing units cannot perform true continuous and precise analog calculations, which restricts their ability to process complex information effectively. To address this, here we develop a DNA computing unit called CALCUL. This system successfully achieves fully analog computation, where all inputs, weighting parameters, and outputs are continuous and precise values. It performs the core operations of a neural network rapidly with high accuracy and is reusable. By integrating magnetic bead technology, we also enable modular operations and the construction of multilayer networks. Ultimately, we use this technology to construct a deep DNA neural network that correctly identifies complex color images with 100% accuracy. These developments provide a robust foundation for building more powerful and precise molecular computers.