Automatic creation of an autonomous agent: genetic evolution of a neural-network driven robot
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
A Taxonomy for artificial embryogeny
Artificial Life
Maximally fault tolerant neural networks
IEEE Transactions on Neural Networks
Complete and partial fault tolerance of feedforward neural nets
IEEE Transactions on Neural Networks
A Cellular Structure for Online Routing of Digital Spiking Neuron Axons and Dendrites on FPGAs
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
The evolutionary emergence of intrinsic regeneration in artificial developing organisms
BioADIT'06 Proceedings of the Second international conference on Biologically Inspired Approaches to Advanced Information Technology
Why are evolved developing organisms also fault-tolerant?
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Spiking neural controllers for pushing objects around
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
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Due to their distributed architecture, artificial neural networks often show a graceful performance degradation to the loss of few units or connections. Living systems also display an additional source of fault-tolerance obtained through distributed processes of self-healing: defective components are actively regenerated. In this paper, we present results obtained with a model of development for spiking neural networks undergoing sustained levels of cell loss. To test their resistance to faults, networks are subjected to random faults during development and mutilated several times during operation. Results show that, evolved to control simulated Khepera robots in a simple navigation task, plastic and non-plastic networks develop fault-tolerant structures which can recover normal operation to various degrees.