Evolving genetic regulatory networks for online neurogenesis

Dennis G. Wilson (1) , Sylvain Cussat-Blanc (1, 2) , Hervé Luga (1, 3)

1 IRIT-REVA - Real Expression Artificial Life
2 UT Capitole - Université Toulouse Capitole
3 UT2J - Université Toulouse - Jean Jaurès

We evolve a Genetic Regulatory Network (GRN) in a three dimensional morphogen gradient environment to determine the topology of the neurons in a Spiking Neural Network (SNN). A genetic algorithm is used to optimize the GRN, selecting individuals based on the performance of the SNN grown by the GRN. Performance is measured on two tasks: visual discrimination and robotic foraging. Early results show potential for this method as both an indirect encoding and on-line regulator of neural networks.

Mots clés: Spiking Neural Network; SNN; GRN; Genetic Regulatory Network; Neural networks

Domaines: Synthèse d'image et réalité virtuelle [cs.GR]; Traitement du signal et de l'image [eess.SP]; Traitement des images [eess.IV]; Vision par ordinateur et reconnaissance de formes [cs.CV]; Intelligence artificielle [cs.AI]

Dennis G. Wilson, Sylvain Cussat-Blanc, Hervé Luga. Evolving genetic regulatory networks for online neurogenesis. 6th Morphogenetic Engineering Workshop (MEW 2016) at ALife XV : Artificial Life Conference, Jul 2016, Cancun, Mexico. pp. 14-15. ⟨hal-01782556⟩