Intrinsic Voltage Offsets in Memcapacitive Biomembranes Enable High-Performance Physical Reservoir Computing

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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 preprocessing costs and restrict real-time deployment. Here, we introduce a heterogeneous memcapacitor-based PRC that exploits internal voltage offsets to enable both monotonic and nonmonotonic input-state correlations crucial for efficient high-dimensional transformations. We demonstrate our approach’s efficacy by predicting a second-order nonlinear dynamical system with a low prediction error (1.80 × 10^–4). 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 the unprecedented performance are major milestones toward high-performance full in-materia PRCs.