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 H\'enon 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.

通过利用内部电压偏移实现各种输入状态相关性，我们引入了一种新颖的基于异质 memcapacitor 的物理蓄水池计算机，该计算机实现了高效的高维变换，显著提高了预测性能。

生物膜中的内在电压偏移实现高性能物理储备计算

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

Magnonic systems have been a major area of research interest due to their
potential benefits in speed and lower power consumption compared to traditional
computing. One particular area that they may be of advantage is as Physical
Reservoir Computers in machine learning models. In this work, we build on an
established design for using an Auto-Oscillation Ring as a reservoir computer
by introducing a simple neural network midstream and introduce an additional
design using a spin wave guide with a scattering regime for processing data
with different types of inputs. We simulate these designs on the new micro
magnetic simulation software, Magnum.np, and show that the designs are capable
of performing on various real world data sets comparably or better than
traditional dense neural networks.

利用新的微磁模拟软件 Magnum.np，通过引入一个简单的神经网络中间层以及使用散射区域的自旋波导，我们在已有的环状自振装置的基础上构建了物理储层计算机，展示出与传统密集神经网络相当或更好的性能，适用于处理不同类型输入的各种真实数据集。