@inproceedings{bffb6e1918374c139c6b40e4980e66bf,
title = "Self-repairing mobile robotic car using astrocyte-neuron networks",
abstract = "A self-repairing robot utilising a spiking astrocyte-neuron network is presented in this paper. It uses the output spike frequency of neurons to control the motor speed and robot activation. A software model of the astrocyte-neuron network previously demonstrated self-detection of faults and its self-repairing capability. In this paper the application demonstrator of mobile robotics is employed to evaluate the fault-tolerant capabilities of the astrocyte-neuron network when implemented in a hardware-based robotic car system. Results demonstrated that when 20% or less synapses associated with a neuron are faulty, the robot car can maintain system performance and complete the task of forward motion correctly. If 80% synapses are faulty, the system performance shows a marginal degradation, however this degradation is much smaller than that of conventional fault-tolerant techniques under the same levels of faults. This is the first time that astrocyte cells merged within spiking neurons demonstrates a self-repairing capabilities in the hardware system for a real application.",
keywords = "Astrocyte, Fault-tolerant, Repair, Robot car, Self-adaptive, Spiking neural networks",
author = "Junxiu Liu and Jim Harkin and Liam McDaid and Halliday, {David M.} and Tyrrell, {Andy M.} and Jon Timmis",
note = "Funding Information: The work is part of the SPANNER project and is funded by EPSRC (EP/N00714X/1). The authors also acknowledge the support of the Intelligent Systems Research Centre at University of Ulster. Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Joint Conference on Neural Networks, IJCNN 2016 ; Conference date: 24-07-2016 Through 29-07-2016",
year = "2016",
month = oct,
day = "31",
doi = "10.1109/IJCNN.2016.7727359",
language = "English",
series = "Proceedings of the International Joint Conference on Neural Networks",
publisher = "IEEE Press",
pages = "1379--1386",
booktitle = "2016 International Joint Conference on Neural Networks, IJCNN 2016",
address = "United States of America",
}