Demonstration of Stochastic Resonance, Population Coding, and Population Voting Using Artificial MoS2 Based Synapses,ACS Nano

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Demonstration of Stochastic Resonance, Population Coding, and Population Voting Using Artificial MoS2 Based Synapses
ACS Nano ( IF 15.8 ) Pub Date : 2021-10-14 , DOI: 10.1021/acsnano.1c05042
Akhil Dodda ₁ , Saptarshi Das _{1,

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Affiliation

Fast detection of weak signals at low energy expenditure is a challenging but inescapable task for the evolutionary success of animals that survive in resource constrained environments. This task is accomplished by the sensory nervous system by exploiting the synergy between three astounding neural phenomena, namely, stochastic resonance (SR), population coding (PC), and population voting (PV). In SR, the constructive role of synaptic noise is exploited for the detection of otherwise invisible signals. In PC, the redundancy in neural population is exploited to reduce the detection latency. Finally, PV ensures unambiguous signal detection even in the presence of excessive noise. Here we adopt a similar strategies and experimentally demonstrate how a population of stochastic artificial neurons based on monolayer MoS₂ field effect transistors (FETs) can use an optimum amount of white Gaussian noise and population voting to detect invisible signals at a frugal energy expenditure (∼10s of nano-Joules). Our findings can aid remote sensing in the emerging era of the Internet of things (IoT) that thrive on energy efficiency.

中文翻译：

使用基于人造二硫化钼的突触演示随机共振、种群编码和种群投票

以低能量消耗快速检测微弱信号对于在资源受限环境中生存的动物的进化成功来说是一项具有挑战性但不可避免的任务。这项任务是由感觉神经系统通过利用三种惊人的神经现象之间的协同作用来完成的，即随机共振 (SR)、群体编码 (PC) 和群体投票 (PV)。在 SR 中，突触噪声的建设性作用被用于检测原本不可见的信号。在 PC 中，利用神经群体中的冗余来减少检测延迟。最后，即使在存在过多噪声的情况下，PV 也能确保明确的信号检测。在这里，我们采用了类似的策略，并通过实验证明了基于单层 MoS ₂的随机人工神经元群体如何场效应晶体管 (FET) 可以使用最佳数量的高斯白噪声和人口投票来检测不可见信号，并且能量消耗较少（约 10 纳焦耳）。我们的研究结果可以在物联网 (IoT) 的新兴时代帮助遥感，该时代因能源效率而蓬勃发展。

更新日期：2021-10-26

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