Molecular neuron: From sensing to logic computation, information encoding, and encryption
Molecular neuron: From sensing to logic computation, information encoding, and encryption
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分子神经元：从传感到逻辑计算、信息编码和加密
DOI：
10.1016/j.snb.2016.08.056
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发表时间：
2017-02-01
影响因子：
8.4
通讯作者：
Li, Nian Bing
中科院分区：
文献类型：
--
作者：
Huang, Wei Tao;Chen, Ling Xiao;Li, Nian Bing
大脑回路令人印象深刻的功能激发了许多科学家尝试通过使用人工分子系统或电子设备来设计神经元类似物。然而，对分子神经元的研究还没有产生同样多种多样的模型。在这里，使用紫外-可见吸收，荧光和共振光散射光谱的pH传感与一个共同的指示剂刚果红染料，我们展示了如何刚果红分子可以用来构建分子神经元和表现出神经元样的行为。我们的分子神经模型使用分子组作为“树突”，其接收和感知环境刺激输入（pH），分子基质作为“索马”，其充当求和函数，并且光学特性的变化作为“轴突”，其代表输出。我们的方法使我们能够利用简单的传感分子作为McCulloch-Pitts神经元的实验实现的大规模逻辑计算在批处理模式，并使用非凡的信息密度固有的分子神经元的字母数字信息编码和分子密码学。我们的研究结果表明，分子可以用作通用的人工神经元，具有感知分析物或环境刺激，记忆分子事件模式和做出决策的能力。(C)© 2016 Elsevier B. V.版权所有。
The impressive functions of brain circuits have inspired many scientists to attempt in designing neuron analogues by using artificial molecular systems or electronic devices. However, the study of molecular neuron has not produced an equal variety of models. Here, using UV-vis absorption, fluorescence, and resonance light scattering spectroscopies for pH sensing with a common indicator Congo red dye, we show how Congo red molecules can be used to construct molecular neuron and exhibit neuron-like behavior. Our molecular neural model uses molecular groups as 'dendrites' which receive and sense environmental stimuli inputs (pH), molecular matrix as 'soma' which acts as the summation function, and the change in optical characteristics as 'axon' which represents outputs. Our approach allows us to utilize simple sensing molecules as McCulloch-Pitts neuron for experimental implementation of large-scale logic computation in batch mode and to use extraordinary information density inherent in molecular neuron for alphanumeric information encoding and molecular cryptography. Our results suggest that molecules could be used as universal artificial neurons with the capability of sensing analytes or environmental stimuli, remembering patterns of molecular events, and making decisions. (C) 2016 Elsevier B.V. All rights reserved.