# Populations of Spiking Neurons for Reservoir Computing: Closed Loop Control of a Compliant Quadruped

arXiv: https://arxiv.org/abs/2004.04560 · HTML: https://mlsanity.com/paper/2004.04560/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Apr 2020.

## Built on by (most cited first, 3 of 3)

- [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning](https://mlsanity.com/paper/2307.15092/) (2023)
- [Composite FORCE learning of chaotic echo state networks for time-series prediction](https://mlsanity.com/paper/2207.02420/) (2022)
- [Generating Oscillation Activity with Echo State Network to Mimic the Behavior of a Simple Central Pattern Generator](https://mlsanity.com/paper/2306.10927/) (2023)

## Abstract (arXiv)

Compliant robots can be more versatile than traditional robots, but their control is more complex. The dynamics of compliant bodies can however be turned into an advantage using the physical reservoir computing frame-work. By feeding sensor signals to the reservoir and extracting motor signals from the reservoir, closed loop robot control is possible. Here, we present a novel framework for implementing central pattern generators with spiking neural networks to obtain closed loop robot control. Using the FORCE learning paradigm, we train a reservoir of spiking neuron populations to act as a central pattern generator. We demonstrate the learning of predefined gait patterns, speed control and gait transition on a simulated model of a compliant quadrupedal robot.