# Neuromorphic Electronic Systems for Reservoir Computing

arXiv: https://arxiv.org/abs/1908.09572 · HTML: https://mlsanity.com/paper/1908.09572/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [Heterogeneous Reservoir Computing Models for Persian Speech Recognition](https://mlsanity.com/paper/2205.12594/) (2022)
- [From Characterization To Construction: Generative Quantum Circuit Synthesis from Gate Set Tomography Data](https://mlsanity.com/paper/2605.01367/) (2026)

## Abstract (arXiv)

This chapter provides a comprehensive survey of the researches and motivations for hardware implementation of reservoir computing (RC) on neuromorphic electronic systems. Due to its computational efficiency and the fact that training amounts to a simple linear regression, both spiking and non-spiking implementations of reservoir computing on neuromorphic hardware have been developed. Here, a review of these experimental studies is provided to illustrate the progress in this area and to address the technical challenges which arise from this specific hardware implementation. Moreover, to deal with challenges of computation on such unconventional substrates, several lines of potential solutions are presented based on advances in other computational approaches in machine learning. Keywords: Analog Microchips, FPGA, Memristors, Neuromorphic Architectures, Reservoir Computing