Memristive physical reservoir computing is a promising approach for solving data classification and temporal processing tasks. This method exploits the nonlinear dynamics of physical, low-power devices to achieve high-dimensional mapping of input signals. Ion-channel-based memristors, which operate with similar voltages, currents, and timescales as biological synapses, are promising due to their rich dynamics, especially for use in biological edge settings. Accurate modeling of their dynamics is essential for optimizing network hyperparameters ex situ to save time and energy. Here, a generalized sigmoidal growth model of ion-channel memristor conductance is presented and shown to be more accurate in predicting dynamics than linear or logistic models. Using the exact solution of the proposed sigmoidal model, the MNIST handwritten digit classification task is optimized and trained ex situ, then tested in situ with the same trained weights. This approach achieved an experimental testing accuracy of 90.6%.

中文翻译：

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具有精确解的非线性忆阻器模型允许进行非原位储层计算训练和原位推理


忆阻物理储层计算是解决数据分类和时间处理任务的一种很有前途的方法。该方法利用物理、低功耗器件的非线性动力学来实现输入信号的高维映射。基于离子通道的忆阻器在与生物突触相似的电压、电流和时间尺度下工作，由于其丰富的动力学而很有前途，特别是用于生物边缘设置。对它们的动力学进行准确建模对于异地优化网络超参数以节省时间和能源至关重要。在这里，提出了离子通道忆阻器电导的广义 S 形增长模型，并表明在预测动力学方面比线性或 Logistic 模型更准确。使用所提出的 S 形模型的精确解，对 MNIST 手写数字分类任务进行优化和非原位训练，然后使用相同的训练权重进行原位测试。这种方法实现了 90.6% 的实验测试准确率。