TY - GEN
T1 - A Modular Spiking Neural Network-Based Neuro-Robotic System for Exploring Embodied Intelligence*
AU - Chen, Zhe
AU - Sun, Tao
AU - Chen, Xie
AU - Shimoda, Shingo
AU - Wang, Huaping
AU - Huang, Qiang
AU - Shi, Qing
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Bio-inspired construction of modular biological neural networks (BNNs) is gaining attention due to their innate stable inter-modular signal transmission ability, which is thought to underlying the emergence of biological intelligence. However, the complicated, laborious fabrication of BNNs with structural and functional connectivity of interest in vitro limits the further exploration of embodied intelligence. In this work, we propose a modular spiking neural network (SNN)-based neuro-robotic system by concurrently running SNN modeling and robot simulation. We show that the modeled mSNNs present complex calcium dynamics resembling mBNNs. In particular, spontaneous periodic network-wide bursts were observed in the mSNN, which could be further suppressed partially or completely with global chemical modulation. Moreover, we demonstrate that after complete suppression, intermodular signal transmission can still be evoked reliably via local stimulation. Therefore, the modeled mSNNs could either achieve reliable trans-modular signal transmission or add adjustable false-positive noise signals (spontaneous bursts). By interconnecting the modeled mSNNs with the simulated mobile robot, active obstacle avoidance and target tracking can be achieved. We further show that spontaneous noise impairs robot performance, which indicates the importance of suppressing spontaneous burst activities of modular networks for the reliable execution of robot tasks. The proposed neuro-robotic system embodies spiking neural networks with a mobile robot to interact with the external world, which paves the way for exploring the arising of more complex biological intelligence.
AB - Bio-inspired construction of modular biological neural networks (BNNs) is gaining attention due to their innate stable inter-modular signal transmission ability, which is thought to underlying the emergence of biological intelligence. However, the complicated, laborious fabrication of BNNs with structural and functional connectivity of interest in vitro limits the further exploration of embodied intelligence. In this work, we propose a modular spiking neural network (SNN)-based neuro-robotic system by concurrently running SNN modeling and robot simulation. We show that the modeled mSNNs present complex calcium dynamics resembling mBNNs. In particular, spontaneous periodic network-wide bursts were observed in the mSNN, which could be further suppressed partially or completely with global chemical modulation. Moreover, we demonstrate that after complete suppression, intermodular signal transmission can still be evoked reliably via local stimulation. Therefore, the modeled mSNNs could either achieve reliable trans-modular signal transmission or add adjustable false-positive noise signals (spontaneous bursts). By interconnecting the modeled mSNNs with the simulated mobile robot, active obstacle avoidance and target tracking can be achieved. We further show that spontaneous noise impairs robot performance, which indicates the importance of suppressing spontaneous burst activities of modular networks for the reliable execution of robot tasks. The proposed neuro-robotic system embodies spiking neural networks with a mobile robot to interact with the external world, which paves the way for exploring the arising of more complex biological intelligence.
UR - http://www.scopus.com/inward/record.url?scp=85208053336&partnerID=8YFLogxK
U2 - 10.1109/ICARM62033.2024.10715795
DO - 10.1109/ICARM62033.2024.10715795
M3 - Conference contribution
AN - SCOPUS:85208053336
T3 - ICARM 2024 - 2024 9th IEEE International Conference on Advanced Robotics and Mechatronics
SP - 1093
EP - 1098
BT - ICARM 2024 - 2024 9th IEEE International Conference on Advanced Robotics and Mechatronics
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 9th IEEE International Conference on Advanced Robotics and Mechatronics, ICARM 2024
Y2 - 8 July 2024 through 10 July 2024
ER -