Synthesizing a Neuron Using Chemical Reactions

Deep learning and synthetic biology are two highly popular fields and draw lots of attentions. Deep learning has been employed for complex problems, and researchers have developed synthetic biology into a powerful tool for More than Moore. However, few works have considered implementing deep neural networks (DNNs) with synthetic biology. In this paper, by revealing the common probability base, we aim to implement the most fundamental element of DNN, a neuron, using chemical reaction networks (CRNs). We firstly propose a computation model in CRNs, then present our architecture of a neuron using such computation model as a basis. The correctness of such computation model and architecture is proved by both mathematical derivation and silico simulation.

[1]  Xiaohu You,et al.  Molecular Synthesis for Probability Theory and Stochastic Process , 2018, J. Signal Process. Syst..

[2]  Lulu Qian,et al.  Supporting Online Material Materials and Methods Figs. S1 to S6 Tables S1 to S4 References and Notes Scaling up Digital Circuit Computation with Dna Strand Displacement Cascades , 2022 .

[3]  R. Jackson,et al.  General mass action kinetics , 1972 .

[4]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[5]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[6]  Luca Cardelli,et al.  From Processes to ODEs by Chemistry , 2008, IFIP TCS.

[7]  Matthew R. Lakin,et al.  Supervised Learning in an Adaptive DNA Strand Displacement Circuit , 2015, DNA.

[8]  Xiaohu You,et al.  A formal design methodology for synthesizing a clock signal with an arbitrary duty cycle of M/N , 2015, 2015 IEEE Workshop on Signal Processing Systems (SiPS).

[9]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[10]  P J Webros.  BACKPROPAGATION THROUGH TIME: WHAT IT DOES AND HOW TO DO IT , 1990 .

[11]  Xiaohu You,et al.  A Formal Combinational Logic Synthesis With Chemical Reaction Networks , 2017, IEEE Transactions on Molecular, Biological and Multi-Scale Communications.

[12]  Erik Winfree,et al.  DNA as a universal substrate for chemical kinetics , 2009, Proceedings of the National Academy of Sciences.

[13]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[14]  Keshab K. Parhi,et al.  Digital logic with molecular reactions , 2013, 2013 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).

[15]  Jehoshua Bruck,et al.  Neural network computation with DNA strand displacement cascades , 2011, Nature.

[16]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[17]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[18]  Xiaohu You,et al.  Efficient fast convolution architectures for convolutional neural network , 2017, 2017 IEEE 12th International Conference on ASIC (ASICON).

[19]  Keshab K. Parhi,et al.  A synthesis flow for digital signal processing with biomolecular reactions , 2010, 2010 IEEE/ACM International Conference on Computer-Aided Design (ICCAD).