Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
A dynamical systems perspective on agent-environment interaction
Artificial Intelligence - Special volume on computational research on interaction and agency, part 1
Evolutionary robotics and the radical envelope-of-noise hypothesis
Adaptive Behavior
Ruggedness and neutrality—the NKp family of fitness landscapes
ALIFE Proceedings of the sixth international conference on Artificial life
Artificial Life
Cambrian intelligence: the early history of the new AI
Cambrian intelligence: the early history of the new AI
Understanding intelligence
The Philosophy of Artificial Life
The Philosophy of Artificial Life
Evolutionary Robotics: The Biology,Intelligence,and Technology
Evolutionary Robotics: The Biology,Intelligence,and Technology
Creatures: an exercise in creation
IEEE Expert: Intelligent Systems and Their Applications
Explaining the Evolved: Homunculi, Modules, and Internal Representation
Proceedings of the First European Workshop on Evolutionary Robotics
Embodied cognition: a field guide
Artificial Intelligence
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Preliminary investigations on the evolvability of a non-spatial GasNet model
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
GasNets and CTRNNs – a comparison in terms of evolvability
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Spatially constrained networks and the evolution of modular control systems
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
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Recent years have seen the discovery of freely diffusing gaseous neurotransmitters, such as nitric oxide (NO), in biological nervous systems. A type of artificial neural network (ANN) inspired by such gaseous signaling, the GasNet, has previously been shown to be more evolvable than traditional ANNs when used as an artificial nervous system in an evolutionary robotics setting, where evolvability means consistent speed to very good solutions — here, appropriate sensorimotor behavior-generating systems. We present two new versions of the GasNet, which take further inspiration from the properties of neuronal gaseous signaling. The plexus model is inspired by the extraordinary NO-producing cortical plexus structure of neural fibers and the properties of the diffusing NO signal it generates. The receptor model is inspired by the mediating action of eurotransmitter receptors. Both models are shown to significantly further improve evolvability. We describe a series of analyses suggesting that the reasons for the increase in evolvability are related to the flexible loose coupling of distinct signaling mechanisms, one "chemical" and one "electrical."