On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification
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 -- su...
| Authors | Rishona Daniels, Duna Wattad, Ronny Ronen et al. |
|---|---|
| Primary category | cs.NE |
| Published | 23 April 2026 |
| Last updated at source | 23 April 2026 |
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