On the dynamics of small continuous-time recurrent neural networks
Adaptive Behavior - Special issue on computational neuroethology
Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
Evolutionary robotics and the radical envelope-of-noise hypothesis
Adaptive Behavior
Information and Computation
Embedded neural networks: exploiting constraints
Neural Networks - Special issue on neural control and robotics: biology and technology
Evolution of Spiking Neural Controllers for Autonomous Vision-Based Robots
ER '01 Proceedings of the International Symposium on Evolutionary Robotics From Intelligent Robotics to Artificial Life
Active vision and feature selection in evolutionary behavioral systems
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Using a net to catch a mate: evolving CTRNNs for the dowry problem
ICSAB Proceedings of the seventh international conference on simulation of adaptive behavior on From animals to animats
Localization of function via lesion analysis
Neural Computation
Emergence of Memory-Driven Command Neurons in Evolved Artificial Agents
Neural Computation
Evolution of homing navigation in a real mobile robot
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Neurocontroller Analysis via Evolutionary Network Minimization
Artificial Life
Evolving Spiking Neural Parameters for Behavioral Sequences
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Evolving spiking networks with variable memristors
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evolving spiking networks with variable memristors
ACM SIGEVOlution
Spiking neural controllers for pushing objects around
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
Evolving spiking networks with variable resistive memories
Evolutionary Computation
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This article investigates the evolution of autonomous agents that perform a memory-dependent counting task. Two types of neurocontrollers are evolved: networks of McCulloch-Pitts neurons, and spiking integrate-and-fire networks. The results demonstrate the superiority of the spiky model in evolutionary success and network simplicity. The combination of spiking dynamics with incremental evolution leads to the successful evolution of agents counting over very long periods. Analysis of the evolved networks unravels the counting mechanism and demonstrates how the spiking dynamics are utilized. Using new measures of spikiness we find that even in agents with spiking dynamics, these are usually truly utilized only when they are really needed, that is, in the evolved subnetwork responsible for counting.