Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
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
Integrate-and-fire neural networks with monosynaptic-like correlated activity
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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This paper addresses the following question: ''What neural circuits can emulate the monosynaptic correlogram generated by a direct connection between two neurons?'' The search for answers to that question has been tackled in two steps: (1) we incorporated into an integrate-and-fire (IAF) neuron model those aspects of neuronal physiology that can influence cross-correlated activity; (2) we evolved networks of biologically realistic neurons towards circuits that are able to generate a monosynaptic correlogram between two neurons. Evolutionary strategies and genetic algorithms were used to explore a computationally intractable search space of physiological parameters and network connectivity. We found that evolutionary strategies perform well in refining good initial solutions, while the simple genetic algorithm achieves worse results even when using a higher computational load. The main obstacles in this challenging study of evolutionary neural networks are exposed and discussed, as well as the results obtained after intensive simulation.