document: Center-crossing recurrent neural networks for the evolution of rhythmic behavior A center-crossing recurrent neural network is one in which the null -LRB- hyper -RRB- surfaces of each neuron intersect at their exact centers of symmetry , ensuring that each neuron 's activation function is centered over the range of net inputs that it receives . We demonstrate that relative to a random initial population , seeding the initial population of an evolutionary search with center-crossing networks significantly improves both the frequency and the speed with which high-fitness oscillatory circuits evolve on a simple walking task . The improvement is especially striking at low mutation variances . Our results suggest that seeding with center-crossing networks may often be beneficial , since a wider range of dynamics is more likely to be easily accessible from a population of center-crossing networks than from a population of random networks
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extractive_keyphrases: symmetry activation function random initial population evolutionary search random networks
abstractive_keyphrases: center-crossing recurrent neural networks rhythmic behavior evolution null surfaces evolutionary algorithm high-fitness oscillatory circuits learning low mutation variance