Evolution of Spiking Neural Networks Robust to Noise and Damage for Control of Simple Animats
One of the central questions of biology is how complex biological systems can continue functioning in the presence of perturbations, damage, and mutational insults. This paper investigates evolution of spiking neural networks, consisting of adaptive exponential neurons. The networks are encoded in linear genomes in a manner inspired by genetic networks. The networks control a simple animat, with two sensors and two actuators, searching for targets in a simple environment. The results show that the presence of noise on the membrane voltage during evolution allows for evolution of efficient control and robustness to perturbations to the value of the neural parameters of neurons.
KeywordsSpiking neural networks Adaptive exponential integrate-and-fire model Genetic algorithm Robustness to noise Robustness to damage
This work was supported by Polish National Science Centre (project EvoSN, UMO-2013/08/M/ST6/00922). I am grateful to Volker Steuber for discussions, and to Ahmed Abdelmotaleb and Michal Joachimczak for their involvement in the development of GReaNs software platform.
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