# Neuromorphic on-chip reservoir computing with spiking neural network architectures

arXiv: https://arxiv.org/abs/2407.20547 · HTML: https://mlsanity.com/paper/2407.20547/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 37 references that are in the map.
- First posted on arXiv 30 Jul 2024.

## Builds on (most cited first, 9 of 9)

- [Going Deeper in Spiking Neural Networks: VGG and Residual Architectures](https://mlsanity.com/paper/1802.02627/) (2018)
- [Backpropagation with Biologically Plausible Spatio-Temporal Adjustment For Training Deep Spiking Neural Networks](https://mlsanity.com/paper/2110.08858/) (2021)
- [The Backpropagation Algorithm Implemented on Spiking Neuromorphic Hardware](https://mlsanity.com/paper/2106.07030/) (2021)
- [Do Reservoir Computers Work Best at the Edge of Chaos?](https://mlsanity.com/paper/2012.01409/) (2020)
- [Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity](https://mlsanity.com/paper/2111.01760/) (2021)
- [A Neural Architecture Search based Framework for Liquid State Machine Design](https://mlsanity.com/paper/2004.07864/) (2020)
- [The Computational Capacity of LRC, Memristive and Hybrid Reservoirs](https://mlsanity.com/paper/2009.00112/) (2020)
- [Memristive Reservoirs Learn to Learn](https://mlsanity.com/paper/2306.12676/) (2023)
- [Structural plasticity on an accelerated analog neuromorphic hardware system](https://mlsanity.com/paper/1912.12047/) (2019)

## Built on by (most cited first, 1 of 1)

- [Knowledge Distillation for Reservoir-based Classifier: Human Activity Recognition](https://mlsanity.com/paper/2505.22985/) (2025)

## Abstract (arXiv)

Reservoir computing is a promising approach for harnessing the computational power of recurrent neural networks while dramatically simplifying training. This paper investigates the application of integrate-and-fire neurons within reservoir computing frameworks for two distinct tasks: capturing chaotic dynamics of the H'enon map and forecasting the Mackey-Glass time series. Integrate-and-fire neurons can be implemented in low-power neuromorphic architectures such as Intel Loihi. We explore the impact of network topologies created through random interactions on the reservoir's performance. Our study reveals task-specific variations in network effectiveness, highlighting the importance of tailored architectures for distinct computational tasks. To identify optimal network configurations, we employ a meta-learning approach combined with simulated annealing. This method efficiently explores the space of possible network structures, identifying architectures that excel in different scenarios. The resulting networks demonstrate a range of behaviors, showcasing how inherent architectural features influence task-specific capabilities. We study the reservoir computing performance using a custom integrate-and-fire code, Intel's Lava neuromorphic computing software framework, and via an on-chip implementation in Loihi. We conclude with an analysis of the energy performance of the Loihi architecture.