# On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification

Source: [On the Role of Preprocessing and Memristor Dynamics in Reservoir Computing for Image Classification](<https://www.sota2.com/research/paper/69eaf93855a3c32af89f8ae3>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-04-25T05:00:04.396Z

Authors: Rishona Daniels, Duna Wattad, Ronny Ronen, David Saad, Shahar Kvatinsky

Published: 2026-04-23T12:23:06.000Z

## Abstract

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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2604.21602>)

[PDF](<https://arxiv.org/pdf/2604.21602.pdf>)