Molecular convolutional neural networks with DNA regulatory circuits

Complex biomolecular circuits enabled cells with intelligent behaviour to survive before neural brains evolved. Since DNA computing was first demonstrated in the mid-1990s, synthetic DNA circuits in liquid phase have been developed as computational hardware to perform neural network-like computations that harness the collective properties of complex biochemical systems. However, scaling up such DNA-based neural networks to support more powerful computation remains challenging. Here we present a systematic molecular implementation of a convolutional neural network algorithm with synthetic DNA regulatory circuits based on a simple switching gate architecture. Our DNA-based weight-sharing convolutional neural network can simultaneously implement parallel multiply–accumulate operations for 144-bit inputs and recognize patterns in up to eight categories autonomously. Further, this system can be connected with other DNA circuits to construct hierarchical networks to recognize patterns in up to 32 categories with a two-step approach: coarse classification on language (Arabic numerals, Chinese oracles, English alphabets and Greek alphabets) followed by classification into specific handwritten symbols. We also reduced the computation time from hours to minutes by using a simple cyclic freeze–thaw approach. Our DNA-based regulatory circuits are a step towards the realization of a molecular computer with high computing power and the ability to classify complex and noisy information.