Reservoir computing (RC) is an emerging recurrent neural network architecture that has attracted growing attention for its low training cost and modest hardware requirements. Memristor-based circuits are particularly promising for RC, as their intrinsic dynamics can reduce network size and parameter overhead in tasks such as time-series prediction and image recognition. Although RC has been demonstrated with several memristive devices, a comprehensive evaluation of device-level requirements remains limited. In this paper, we analyze and explain the operation of a parallel delayed feedback network (PDFN) RC architecture with volatile memristors, focusing on how device characteristics -- such as decay rate, quantization, and variability -- affect reservoir performance. We further discuss strategies to improve data representation in the reservoir using preprocessing methods and suggest potential improvements. The proposed approach achieves 95.89% classification accuracy on MNIST, comparable with the best reported memristor-based RC implementations. Furthermore, the method maintains high robustness under 20% device variability, achieving an accuracy of up to 94.2%. These results demonstrate that volatile memristors can support reliable spatio-temporal information processing and reinforce their potential as key building blocks for compact, high-speed, and energy-efficient neuromorphic computing systems.

翻译：储层计算（RC）是一种新兴的递归神经网络架构，因其训练成本低、硬件要求适中等优势而备受关注。基于忆阻器的电路在RC中尤其具有潜力，其固有动力学特性可在时间序列预测、图像识别等任务中减小网络规模并降低参数开销。尽管已有多种忆阻器件实现了RC功能，但对器件级需求的系统性评估仍十分有限。本文分析并阐释了采用易失性忆阻器的并行延迟反馈网络RC架构的工作原理，重点研究了衰减率、量化精度与器件变异等器件特性对储层性能的影响机制。我们进一步探讨了通过预处理方法改善储层数据表示的策略，并提出了潜在改进方向。所提方法在MNIST数据集上实现了95.89%的分类准确率，与已报道的最优忆阻器RC实现性能相当。此外，在20%器件变异条件下，该方法仍保持高达94.2%的准确率，展现出强鲁棒性。这些结果表明，易失性忆阻器能够支撑可靠的时空信息处理，进一步验证了其作为构建紧凑、高速、节能神经形态计算系统关键元件的潜力。