Reservoir computing is a highly efficient machine learning framework for processing temporal data by extracting features from the input signal and mapping them into higher dimensional spaces. Physical reservoir layers have been realized using spintronic oscillators, atomic switch networks, silicon photonic modules, ferroelectric transistors, and volatile memristors. However, these devices are intrinsically energy-dissipative due to their resistive nature, which leads to increased power consumption. Therefore, capacitive memory devices can provide a more energy-efficient approach. Here, we leverage volatile biomembrane-based memcapacitors that closely mimic certain short-term synaptic plasticity functions as reservoirs to solve classification tasks and analyze time-series data in simulation and experimentally. Our system achieves a 99.6% accuracy rate for spoken digit classification and a normalized mean square error of 7.81*10^{-4} in a second-order non-linear regression task. Furthermore, to showcase the device's real-time temporal data processing capability, we achieve 100% accuracy for a real-time epilepsy detection problem from an inputted electroencephalography (EEG) signal. Most importantly, we demonstrate that each memcapacitor consumes an average of 41.5 fJ of energy per spike, regardless of the selected input voltage pulse width, while maintaining an average power of 415 fW for a pulse width of 100 ms. These values are orders of magnitude lower than those achieved by state-of-the-art memristors used as reservoirs. Lastly, we believe the biocompatible, soft nature of our memcapacitor makes it highly suitable for computing and signal-processing applications in biological environments.

翻译：储层计算是一种高效处理时序数据的机器学习框架，其原理是从输入信号中提取特征并将其映射到高维空间。目前已利用自旋振荡器、原子开关网络、硅光子模块、铁电晶体管和易失性忆阻器实现了物理储层。然而，这些器件因其电阻特性而本质上是能量耗散的，导致功耗增加。因此，电容型存储器件可提供更高能效的替代方案。本文利用基于生物膜的易失性忆容器（其功能高度模拟特定短时突触可塑性）作为储层，通过仿真和实验解决分类任务并分析时序数据。我们的系统在口语数字分类任务中达到99.6%的准确率，在二阶非线性回归任务中获得7.81×10^{-4}的归一化均方误差。此外，为展示器件的实时时序数据处理能力，我们通过输入脑电图（EEG）信号实现了100%的实时癫痫检测准确率。最重要的是，我们证明每个忆容器在输入电压脉冲宽度任意时，每脉冲平均能耗仅为41.5 fJ，且在100 ms脉冲宽度下平均功耗为415 fW。这些值比当前最先进的忆阻器储层低数个数量级。最后，我们认为忆容器的生物兼容性和柔软特性使其特别适用于生物环境中的计算和信号处理应用。