Nonlinear decision-making with enzymatic neural networks

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Okumura, Shu | Gines, Guillaume | Lobato-Dauzier, Nicolas | Baccouche, Alexandre | Deteix, Robin | Fujii, Teruo | Rondelez, Yannick | Genot, Anthony, J

Edité par CCSD ; Nature Publishing Group -

International audience. Artificial neural networks have revolutionized electronic computing. Similarly, molecular networks with neuromorphic architectures may enable molecular decision-making on a level comparable to gene regulatory networks 1-4. Nonenzymatic networks could in principle support neuromorphic architectures, and seminal proof-of-principles have been reported 5,6. However, leakages, as well as issues with sensitivity, speed, nonlinearities and preparation, make the composition of layers delicate, and molecular classifications equivalent to a multilayer neural network (e.g. nonlinear partitioning of a concentration space) remain elusive. Here we introduce DNA-encoded enzymatic neurons with tunable weights and biases, and which are assembled in multilayer architectures to classify nonlinearly separable regions. We first leverage the sharp decision margin of the neurons to compute various majority functions on 10 bits. We then compose neurons into a twolayer network, and synthetize a parametric family of rectangular functions on a microRNA input. Finally, we connect neural and logical computations into a hybrid circuit that recursively partitions a concentration plane according to a decision tree in cell-sized droplets. This computational power and extreme miniaturization open avenues to query and manage molecular systems with complex contents, such as liquid biopsies or DNA databases.

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