Here, we adapted the basic concept of artificial neural networks (ANN) and experimentally demonstrate a broadly applicable single layer ANN type architecture with molecular engineered bacteria to perform complex irreversible computing like multiplexing, de-multiplexing, encoding, decoding, majority functions, and reversible computing like Feynman and Fredkin gates. The encoder and majority functions and reversible computing were experimentally implemented within living cells for the first time. We created molecular-devices, which worked as artificial neuro-synapses in bacteria, where input chemical signals were linearly combined and processed through a non-linear activation function to produce fluorescent protein outputs. To create such molecular devices, we established a set of rules by corelating truth tables, mathematical equations of ANN, and molecular-device design, which unlike molecular computing, does not require circuit diagram and the equation directly correlates the design of the molecular-device. To our knowledge this is the first adaptation of ANN type architecture with engineered cells. This work may have significance in new platform for biomolecular computing, reversible computing and in transforming living cells as ANN-enabled hardware.

中文翻译：

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具有分子工程细菌的单层人工神经网络类型架构，用于复杂的常规和可逆计算

在这里，我们采用了人工神经网络 (ANN) 的基本概念，并通过实验证明了具有分子工程细菌的广泛适用的单层 ANN 类型架构，以执行复杂的不可逆计算，如复用、解复用、编码、解码、多数函数和可逆计算。像费曼和弗雷德金门一样的计算。编码器和多数函数以及可逆计算首次在活细胞中实现。我们创造了分子装置，它在细菌中充当人工神经突触，其中输入的化学信号被线性组合并通过非线性激活函数进行处理，以产生荧光蛋白输出。为了创建这样的分子装置，我们通过关联真值表、人工神经网络的数学方程、与分子计算不同，分子装置设计不需要电路图，方程与分子装置的设计直接相关。据我们所知，这是 ANN 类型架构与工程单元的首次适配。这项工作可能对生物分子计算、可逆计算和将活细胞转化为支持 ANN 的硬件的新平台具有重要意义。