Bridging neuroscience and robotics: spiking neural networks in action
Article
Jones, A., Gandhi, V., Mahiddine, A. and Huyck, C. 2023. Bridging neuroscience and robotics: spiking neural networks in action. Sensors. 23 (21), pp. 1-14. https://doi.org/10.3390/s23218880
Type | Article |
---|---|
Title | Bridging neuroscience and robotics: spiking neural networks in action |
Authors | Jones, A., Gandhi, V., Mahiddine, A. and Huyck, C. |
Abstract | Robots are becoming increasingly sophisticated in the execution of complex tasks. However, an area that requires development is the ability to act in dynamically changing environments. To advance this, developments have turned towards understanding the human brain and applying this to improve robotics. The present study used electroencephalogram (EEG) data recorded from 54 human participants whilst they performed a two-choice task. A build-up of motor activity starting around 400 ms before response onset, also known as the lateralized readiness potential (LRP), was observed. This indicates that actions are not simply binary processes but rather, response-preparation is gradual and occurs in a temporal window that can interact with the environment. In parallel, a robot arm executing a pick-and-place task was developed. The understanding from the EEG data and the robot arm were integrated into the final system, which included cell assemblies (CAs)—a simulated spiking neural network—to inform the robot to place the object left or right. Results showed that the neural data from the robot simulation were largely consistent with the human data. This neurorobotics study provides an example of how to integrate human brain recordings with simulated neural networks in order to drive a robot. |
Keywords | cell assemblies; lateralized readiness potential; LRP; spiking neural network; robot; Humans; Robotics; Neural Networks, Computer; Brain - physiology; Computer Simulation; Electroencephalography; Electrical and Electronic Engineering; Biochemistry; Instrumentation; Atomic and Molecular Physics, and Optics; Analytical Chemistry |
Sustainable Development Goals | 9 Industry, innovation and infrastructure |
Middlesex University Theme | Health & Wellbeing |
Publisher | MDPI AG |
Journal | Sensors |
ISSN | |
Electronic | 1424-8220 |
Publication dates | |
Online | 01 Nov 2023 |
01 Nov 2023 | |
Publication process dates | |
Submitted | 11 Sep 2023 |
Accepted | 27 Oct 2023 |
Deposited | 13 Nov 2023 |
Output status | Published |
Publisher's version | License |
Copyright Statement | Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/) |
Additional information | This article belongs to the Special Issue Neuro-Robotics Systems: Sensing, Cognition, Learning, and Control |
Digital Object Identifier (DOI) | https://doi.org/10.3390/s23218880 |
PubMed ID | 37960579 |
PubMed Central ID | PMC10647810 |
Web of Science identifier | WOS:001100439000001 |
National Library of Medicine ID | 101204366 |
Language | English |
https://repository.mdx.ac.uk/item/w72z2
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