忆容生物膜中的固有电压偏移实现高性能物理储备池计算
Intrinsic Voltage Offsets in Memcapacitive Bio-Membranes Enable High-Performance Physical Reservoir Computing
AI总结：
本研究提出利用忆容生物膜固有电压偏移的异质忆容器基物理储备池计算方法，无需输入编码即可实现低误差时序预测，还可推广至其他神经形态器件，为高性能全材料内储备池计算奠定基础。
摘要：
储备池计算（Reservoir Computing）是一种受大脑启发的机器学习框架，通过将输入映射到高维空间来处理时序数据。物理储备池计算机（PRC）利用物理基底中的固有衰减记忆和非线性特性（包括原子开关、光子学、易失性忆阻器，以及近期出现的忆容器）来实现高效的高维映射。传统PRC通常由同质器件阵列构成，依赖输入编码方法和较大的器件间随机差异来提升非线性和高维映射能力，这些方法会带来高昂的预处理成本，限制了实时部署。本文提出一种新型异质忆容器基PRC，利用内部电压偏移产生单调与非单调的输入-状态关联，这对高效高维变换至关重要。我们通过预测二阶非线性动力系统验证了该方法的有效性，预测误差极低（0.00018）；此外还对混沌Hénon映射进行预测，取得了较低的归一化均方根误差（0.080）。与以往PRC不同，这些误差无需输入编码方法即可实现，凸显了差异化输入-状态关联的优势。最重要的是，我们将该方法推广至其他无固有电压偏移的神经形态器件，通过外加偏移实现多种输入-状态关联。本研究的方法与前所未有的性能是迈向高性能全材料内PRC的重要里程碑。
英文摘要：
Reservoir computing is a brain-inspired machine learning framework for processing temporal data by mapping inputs into high-dimensional spaces. Physical reservoir computers (PRCs) leverage native fading memory and nonlinearity in physical substrates, including atomic switches, photonics, volatile memristors, and, recently, memcapacitors, to achieve efficient high-dimensional mapping. Traditional PRCs often consist of homogeneous device arrays, which rely on input encoding methods and large stochastic device-to-device variations for increased nonlinearity and high-dimensional mapping. These approaches incur high pre-processing costs and restrict real-time deployment. Here, we introduce a novel heterogeneous memcapacitor-based PRC that exploits internal voltage offsets to enable both monotonic and non-monotonic input-state correlations crucial for efficient high-dimensional transformations. We demonstrate our approach's efficacy by predicting a second-order nonlinear dynamical system with an extremely low prediction error (0.00018). Additionally, we predict a chaotic Hénon map, achieving a low normalized root mean square error (0.080). Unlike previous PRCs, such errors are achieved without input encoding methods, underscoring the power of distinct input-state correlations. Most importantly, we generalize our approach to other neuromorphic devices that lack inherent voltage offsets using externally applied offsets to realize various input-state correlations. Our approach and unprecedented performance are a major milestone towards high-performance full in-materia PRCs.
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