IEEE Trans Nanobioscience. 2020 Apr;19(2):224-236. doi: 10.1109/TNB.2020.2975942. Epub 2020 Feb 24.
With the advancement of synthetic biology, several new tools have been conceptualized over the years as alternative treatments for current medical procedures. As part of this work, we investigate how synthetically engineered neurons can operate as digital logic gates that can be used towards bio-computing inside the brain and its impact on epileptic seizure-like behaviour. We quantify the accuracy of logic gates under high firing rates amid a network of neurons and by how much it can smooth out uncontrolled neuronal firings. To test the efficacy of our method, simulations composed of computational models of neurons connected in a structure that represents a logic gate are performed. Our simulations demonstrate the accuracy of performing the correct logic operation, and how specific properties such as the firing rate can play an important role in the accuracy. As part of the analysis, the mean squared error is used to quantify the quality of our proposed model and predict the accurate operation of a gate based on different sampling frequencies. As an application, the logic gates were used to smooth out epileptic seizure-like activity in a biological neuronal network, where the results demonstrated the effectiveness of reducing its mean firing rate. Our proposed system has the potential to be used in future approaches to treating neurological conditions in the brain.
随着合成生物学的进步,近年来已经提出了几种新的工具,作为对当前医学程序的替代治疗方法。作为这项工作的一部分,我们研究了合成工程神经元如何作为数字逻辑门运作,这些逻辑门可以用于大脑内部的生物计算以及它们对癫痫样发作行为的影响。我们量化了在神经元网络中高发射率下以及它能在多大程度上平滑失控神经元发射的情况下逻辑门的准确性。为了测试我们方法的有效性,对由连接成代表逻辑门的结构的神经元的计算模型组成的模拟进行了执行。我们的模拟演示了执行正确逻辑操作的准确性,以及诸如发射率等特定属性如何在准确性中发挥重要作用。在分析过程中,均方误差用于量化我们提出的模型的质量,并根据不同的采样频率预测门的准确操作。作为应用,逻辑门被用于平滑生物神经元网络中的癫痫样活动,结果表明降低其平均发射率是有效的。我们提出的系统有可能用于未来治疗大脑中神经状况的方法。