# Generating Oscillation Activity with Echo State Network to Mimic the Behavior of a Simple Central Pattern Generator

arXiv: https://arxiv.org/abs/2306.10927 · HTML: https://mlsanity.com/paper/2306.10927/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 19 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies](https://mlsanity.com/paper/2010.00951/) (2020)
- [Analysis of structure and dynamics in three-neuron motifs](https://mlsanity.com/paper/1811.05225/) (2018)
- [Populations of Spiking Neurons for Reservoir Computing: Closed Loop Control of a Compliant Quadruped](https://mlsanity.com/paper/2004.04560/) (2020)
- [Neuromorphic adaptive spiking CPG towards bio-inspired locomotion of legged robots](https://mlsanity.com/paper/2101.09709/) (2021)

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

- [A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning](https://mlsanity.com/paper/2307.15092/) (2023)

## Abstract (arXiv)

This paper presents a method for reproducing a simple central pattern generator (CPG) using a modified Echo State Network (ESN). Conventionally, the dynamical reservoir needs to be damped to stabilize and preserve memory. However, we find that a reservoir that develops oscillatory activity without any external excitation can mimic the behaviour of a simple CPG in biological systems. We define the specific neuron ensemble required for generating oscillations in the reservoir and demonstrate how adjustments to the leaking rate, spectral radius, topology, and population size can increase the probability of reproducing these oscillations. The results of the experiments, conducted on the time series simulation tasks, demonstrate that the ESN is able to generate the desired waveform without any input. This approach offers a promising solution for the development of bio-inspired controllers for robotic systems.