Waves, bumps, and patterns in neural field theories
Biological Cybernetics
Journal of Cognitive Neuroscience
Modeling of Cortical Signals Using Optimized Echo State Networks with Leaky Integrator Neurons
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
Comparing results of 31 algorithms from the black-box optimization benchmarking BBOB-2009
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
Heterogeneous particle swarm optimization
ANTS'10 Proceedings of the 7th international conference on Swarm intelligence
Can self-organisation emerge through dynamic neural fields computation?
Connection Science
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Due to their strong non-linear behavior, optimizing the parameters of dynamic neural fields is particularly challenging and often relies on expert knowledge and trial and error. In this paper, we study the ability of particle swarm optimization (PSO) and covariance matrix adaptation (CMA-ES) to solve this problem when scenarios specifying the input feeding the field and desired output profiles are provided. A set of spatial lower and upper bounds, called templates are introduced to define a set of desired output profiles. The usefulness of the method is illustrated on three classical scenarios of dynamic neural fields: competition, working memory and tracking.