Projects per year
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.
|Title of host publication||2016 International Joint Conference on Neural Networks, IJCNN 2016|
|Number of pages||8|
|Publication status||Published - 31 Oct 2016|
|Event||2016 International Joint Conference on Neural Networks, IJCNN 2016 - Vancouver, Canada|
Duration: 24 Jul 2016 → 29 Jul 2016
|Name||Proceedings of International Joint Conference on Neural Networks|
|Conference||2016 International Joint Conference on Neural Networks, IJCNN 2016|
|Period||24/07/16 → 29/07/16|
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- Robot car
- Spiking neural networks
- 1 Finished
Self-repairing hardware paradigms based on astrocyte-neuron models
Halliday, D. M., Timmis, J. & Tyrrell, A.
1/10/15 → 31/10/19
Project: Research project (funded) › Research