The abstract mathematical rules of artificial neural network (ANN) are implemented through computation using electronic computers, photonics and in-vitro DNA computation. Here we demonstrate the physical realization of ANN in living bacterial cells. We created a single layer ANN using engineered bacteria, where a single bacterium works as an artificial neuron and demonstrated a 2-to-4 decoder and a 1-to-2 de-multiplexer for processing chemical signals. The inputs were extracellular chemical signals, which linearly combined and got processed through a non-linear log-sigmoid activation function to produce fluorescent protein outputs. The activation function was generated by synthetic genetic circuits, and for each artificial neuron, the weight and bias values were adjusted manually by engineering the molecular interactions within the bacterial neuron to represent a specific logical function. The artificial bacterial neurons were connected as ANN architectures to implement a 2-to-4 chemical decoder and a 1-to-2 chemical de-multiplexer. To our knowledge, this is the first ANN created by artificial bacterial neurons. Thus, it may open up a new direction in ANN research, where engineered biological cells can be used as ANN enabled hardware.

中文翻译：

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带有工程细菌的单层人工神经网络

人工神经网络 (ANN) 的抽象数学规则是通过使用电子计算机、光子学和体外 DNA 计算的计算来实现的。在这里，我们展示了 ANN 在活细菌细胞中的物理实现。我们使用工程细菌创建了单层人工神经网络，其中单个细菌充当人工神经元，并展示了用于处理化学信号的 2 对 4 解码器和 1 对 2 多路分解器。输入是细胞外化学信号，它们线性组合并通过非线性 log-sigmoid 激活函数进行处理，以产生荧光蛋白输出。激活函数是由合成遗传电路生成的，对于每个人工神经元，通过设计细菌神经元内的分子相互作用来表示特定的逻辑功能，手动调整权重和偏差值。人工细菌神经元连接为 ANN 架构，以实现 2 对 4 化学解码器和 1 对 2 化学解复用器。据我们所知，这是第一个由人工细菌神经元创建的人工神经网络。因此，它可能会开辟 ANN 研究的新方向，其中工程生物细胞可以用作支持 ANN 的硬件。