Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
Automatic definition of modular neural networks
Adaptive Behavior
On contraction analysis for non-linear systems
Automatica (Journal of IFAC)
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Spiking Neuron Models: An Introduction
Spiking Neuron Models: An Introduction
Evolution of Plastic Control Networks
Autonomous Robots
Evolving neural networks through augmenting topologies
Evolutionary Computation
Evolutionary approaches to neural control of rolling, walking, swimming and flying animats or robots
Biologically inspired robot behavior engineering
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Measuring, enabling and comparing modularity, regularity and hierarchy in evolutionary design
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolution of spiking neural circuits in autonomous mobile robots: Research Articles
International Journal of Intelligent Systems - Intentional Dynamic Systems—Foundations, Modeling, and Robot Implementation
Journal of Cognitive Neuroscience
Emergence of attention within a neural population
Neural Networks
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Dynamic neural field with local inhibition
Biological Cybernetics
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
Bio-Inspired Artificial Intelligence: Theories, Methods, and Technologies
Evolving modular neural-networks through exaptation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A distributed model of spatial visual attention
Biomimetic Neural Learning for Intelligent Robots
On the relationships between synaptic plasticity and generative systems
Proceedings of the 13th annual conference on Genetic and evolutionary computation
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Neuro-evolution and computational neuroscience are two scientific domains that produce surprisingly different artificial neural networks. Inspired by the "toolbox" used by neuroscientists to create their models, this paper argues two main points: (1) neural maps (spatially-organized identical neurons) should be the building blocks to evolve neural networks able to perform cognitive functions and (2) well-identified modules of the brain for which there exists computational neuroscience models provide well-defined benchmarks for neuro-evolution. To support these claims, a method to evolve networks of neural maps is introduced then applied to evolve neural networks with a similar functionality to basal ganglia in animals (i.e. action selection). Results show that: (1) the map-based encoding easily achieves this task while a direct encoding never solves it; (2) this encoding is independent of the size of maps and can therefore be used to evolve large and brain-like neural networks; (3) the failure of direct encoding to solve the task validates the relevance of action selection as a benchmark for neuro-evolution