Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolving networks of integrate-and-fire neurons
Neurocomputing
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To study the physiology of the central nervous system it is necessary to understand the properties of the neural networks that integrate it and conform its functional substratum. Modeling and simulation of neural networks allow us to face this problem and consider it from the point of view of the analysis of activity correlation between pairs of neurons. In this paper, we define an optimized integrate-and-fire model of the simplest network possible, the monosynaptic circuit, and we raise the problem of searching for alternative non-monosynaptic circuits that generate monosynaptic-like correlated activity. For this purpose, we design an evolutionary algorithm with a crossover-with-speciation operator that works on populations of neural networks. The optimization of the neuronal model and the concurrent execution of the simulations allow us to efficiently cover the search space to finally obtain networks with monosynaptic-like correlated activity.