Abstract
How can we build robot controllers that are able to work under harsh conditions, but without experiencing catastrophic failures? As seen on the recent Fukushima’s nuclear disaster, standard robots break down when exposed to high radiation environments. Here we present the results from two arrangements of Spiking Neural Networks, based on the Liquid State Machine (LSM) framework, that were able to gracefully degrade under the effects of a noisy current injected directly into each simulated neuron. These noisy currents could be seen, in a simplified way, as the consequences of exposition to non-destructive radiation. The results show that not only can the systems withstand noise, but one of the configurations, the Modular Parallel LSM, actually improved its results, in a certain range, when the noise levels were increased. Also, the robot controllers implemented in this work are suitable to run on a modern, power efficient neuromorphic hardware such as SpiNNaker.
DOI
10.1007/978-3-319-46687-3_21
Publication Date
2016-09-29
Publication Title
Neural Information Processing
Publisher
Springer International Publishing
ISBN
9783319466866
ISSN
1611-3349
Embargo Period
2024-11-22
First Page
195
Last Page
204
Recommended Citation
de Azambuja, R., Klein, F., Stoelen, M., Adams, S., & Cangelosi, A. (2016) 'Graceful Degradation Under Noise on Brain Inspired Robot Controllers', Neural Information Processing, , pp. 195-204. Springer International Publishing: Available at: https://doi.org/10.1007/978-3-319-46687-3_21
