Neuromorphic building blocks for locomotion pattern generation
Conference paper
Yang, Z. and Gandhi, V. 2022. Neuromorphic building blocks for locomotion pattern generation. 2022 International Conference on Machine Learning, Control, and Robotics (MLCR). Suzhou, China 29 - 31 Oct 2022 IEEE. https://doi.org/10.1109/MLCR57210.2022.00010
Type | Conference paper |
---|---|
Title | Neuromorphic building blocks for locomotion pattern generation |
Authors | Yang, Z. and Gandhi, V. |
Abstract | The central pattern generators network (CPGs) plays an important role in motion control which enables creatures to interact with the world. A novel neuromorphic circuit model presented in this work can be used as the simple building blocks for prescribing more complex, coordinated motor patterns. The circuit demonstrates its capability in generating the activity frequency and duty cycle, independently adjustable by a small set of model parameters. The simulation outcomes also show that the circuit can implement the parallel and distributed algorithms for building the artificial CPGs to drive motors. |
Middlesex University Theme | Health & Wellbeing |
Conference | 2022 International Conference on Machine Learning, Control, and Robotics (MLCR) |
ISBN | |
Electronic | 978-1-6654-5459-9 |
Publisher | IEEE |
Publication dates | |
29 Oct 2022 | |
Online | 08 Feb 2023 |
Publication process dates | |
Deposited | 07 Nov 2022 |
Accepted | 15 Jul 2022 |
Output status | Published |
Accepted author manuscript | |
Copyright Statement | Copyright © 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
Digital Object Identifier (DOI) | https://doi.org/10.1109/MLCR57210.2022.00010 |
Language | English |
https://repository.mdx.ac.uk/item/8q24y
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