School of Computer Science and Electronic Engineering, University of Essex, Colchester, UK.
BioMediTech, Faculty of Medicine and Health Technology, Tampere University, P.O.Box 553, 33101, Tampere, Finland.
Sci Rep. 2021 Jan 12;11(1):595. doi: 10.1038/s41598-020-79891-x.
This paper proposes the use of astrocytes to realize Boolean logic gates, through manipulation of the threshold of [Formula: see text] ion flows between the cells based on the input signals. Through wet-lab experiments that engineer the astrocytes cells with pcDNA3.1-hGPR17 genes as well as chemical compounds, we show that both AND and OR gates can be implemented by controlling [Formula: see text] signals that flow through the population. A reinforced learning platform is also presented in the paper to optimize the [Formula: see text] activated level and time slot of input signals [Formula: see text] into the gate. This design platform caters for any size and connectivity of the cell population, by taking into consideration the delay and noise produced from the signalling between the cells. To validate the effectiveness of the reinforced learning platform, a [Formula: see text] signalling simulator was used to simulate the signalling between the astrocyte cells. The results from the simulation show that an optimum value for both the [Formula: see text] activated level and time slot of input signals [Formula: see text] is required to achieve up to 90% accuracy for both the AND and OR gates. Our method can be used as the basis for future Neural-Molecular Computing chips, constructed from engineered astrocyte cells, which can form the basis for a new generation of brain implants.
本文提出利用星形胶质细胞通过操纵基于输入信号的细胞间[Formula: see text]离子流的阈值来实现布尔逻辑门。通过对带有 pcDNA3.1-hGPR17 基因的星形胶质细胞进行工程改造以及使用化学化合物的湿实验室实验，我们展示了通过控制流经群体的[Formula: see text]信号，可以实现与门和或门。本文还提出了一个强化学习平台，用于优化输入信号[Formula: see text]进入门的[Formula: see text]激活水平和时隙。该设计平台考虑了细胞间信号传递产生的延迟和噪声，适用于任何大小和连接性的细胞群体。为了验证强化学习平台的有效性，使用[Formula: see text]信号模拟器模拟了星形胶质细胞之间的信号传递。模拟结果表明，对于与门和或门，输入信号[Formula: see text]的[Formula: see text]激活水平和时隙都需要一个最佳值，才能达到高达 90%的准确性。我们的方法可以作为未来基于工程星形胶质细胞的神经-分子计算芯片的基础，为新一代脑植入物奠定基础。
