论文标题

具有工程细菌的单层人工神经网络

A single layer artificial neural network with engineered bacteria

论文作者

Sarkar, Kathakali, Bonnerjee, Deepro, Bagh, Sangram

论文摘要

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,我们证明了活细胞细胞中ANN的物理实现。我们使用工程细菌创建了一个单层ANN,其中单个细菌作为人造神经元起作用,并展示了一个2到4解码器和1-2个用于处理化学信号的1-2驱动器。输入是细胞外化学信号,它们通过非线性对数sigmoid激活函数进行线性组合并处理,以产生荧光蛋白质输出。激活函数是通过合成遗传回路产生的,对于每个人造神经元,通过工程化细菌神经元内的分子相互作用来指定特定逻辑函数,对重量和偏置值进行了调整。人工细菌神经元作为ANN体系结构连接起来,以实施2到4的化学解码器和1-2化学脱氧剂。据我们所知,这是人造细菌神经元创建的第一个ANN。因此,它可能会在ANN研究中打开一个新的方向,在该研究中,可以将工程化的生物细胞用作ANN启用硬件。

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.

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