Efficient and accurate prediction of physical systems is important even when the rules of those systems cannot be easily learned. Reservoir computing, a type of recurrent neural network with fixed nonlinear units, is one such prediction method and is valued for its ease of training. Organic electrochemical transistors (OECTs) are physical devices with nonlinear transient properties that can be used as the nonlinear units of a reservoir computer. We present a theoretical framework for simulating reservoir computers using OECTs as the non-linear units as a test bed for designing physical reservoir computers. We present a proof of concept demonstrating that such an implementation can accurately predict the Lorenz attractor with comparable performance to standard reservoir computer implementations. We explore the effect of operating parameters and find that the prediction performance strongly depends on the pinch-off voltage of the OECTs.

翻译：高效且准确地预测物理系统至关重要，即使这些系统的规则不易学习。储层计算作为一种具有固定非线性单元的循环神经网络，正是这样一种预测方法，并因其易于训练而受到重视。有机电化学晶体管是具有非线性瞬态特性的物理器件，可用作储层计算机的非线性单元。我们提出了一个理论框架，用于模拟以OECT作为非线性单元的储层计算机，作为设计物理储层计算机的测试平台。我们通过概念验证证明，这种实现方式能够准确预测洛伦茨吸引子，其性能与标准储层计算机实现相当。我们探讨了工作参数的影响，发现预测性能在很大程度上取决于OECT的夹断电压。