# Memristive Reservoirs Learn to Learn

arXiv: https://arxiv.org/abs/2306.12676 · HTML: https://mlsanity.com/paper/2306.12676/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 49 references that are in the map.
- First posted on arXiv 22 Jun 2023.

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

- [Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks](https://mlsanity.com/paper/1703.03400/) (2017)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [Learning to learn by gradient descent by gradient descent](https://mlsanity.com/paper/1606.04474/) (2016)
- [Evolution Strategies as a Scalable Alternative to Reinforcement Learning](https://mlsanity.com/paper/1703.03864/) (2017)
- [Long short-term memory and learning-to-learn in networks of spiking neurons](https://mlsanity.com/paper/1803.09574/) (2018)
- [SpikeGPT: Generative Pre-trained Language Model with Spiking Neural Networks](https://mlsanity.com/paper/2302.13939/) (2023)
- [Neuromorphic Hardware learns to learn](https://mlsanity.com/paper/1903.06493/) (2019)
- [Reservoirs learn to learn](https://mlsanity.com/paper/1909.07486/) (2019)
- [Relational representation learning with spike trains](https://mlsanity.com/paper/2205.09140/) (2022)

## Built on by (most cited first, 4 of 4)

- [Training-free AI for Earth Observation Change Detection using Physics Aware Neuromorphic Networks](https://mlsanity.com/paper/2506.04285/) (2025)
- [Dynamic Reservoir Computing with Physical Neuromorphic Networks](https://mlsanity.com/paper/2505.16813/) (2025)
- [Neuromorphic on-chip reservoir computing with spiking neural network architectures](https://mlsanity.com/paper/2407.20547/) (2024)
- [Learning Chaotic Dynamics with Neuromorphic Network Dynamics](https://mlsanity.com/paper/2506.10773/) (2025)

## Abstract (arXiv)

Memristive reservoirs draw inspiration from a novel class of neuromorphic hardware known as nanowire networks. These systems display emergent brain-like dynamics, with optimal performance demonstrated at dynamical phase transitions. In these networks, a limited number of electrodes are available to modulate system dynamics, in contrast to the global controllability offered by neuromorphic hardware through random access memories. We demonstrate that the learn-to-learn framework can effectively address this challenge in the context of optimization. Using the framework, we successfully identify the optimal hyperparameters for the reservoir. This finding aligns with previous research, which suggests that the optimal performance of a memristive reservoir occurs at the `edge of formation' of a conductive pathway. Furthermore, our results show that these systems can mimic membrane potential behavior observed in spiking neurons, and may serve as an interface between spike-based and continuous processes.