Artificial evolution of pulsed neural networks on the motion pattern classification system
Yoshiaki Katada, Kazuhiro Ohkura, Kazushi Ueda · 2004
Categorization is one of the most important cognitive abilities for autonomous agents. In natural systems, animals discriminate any object not only by its figure but also by its motion pattern. In this work, we applied the standard GA to evolve pulsed neural controllers for the motion pattern classification system in order to investigate how evolved agents perform the discrimination task, its evolutionary dynamics and the process of self-organization in the neural controllers. The results demonstrate that the agent controlled by the evolved neural networks can discriminate between the objects with the different motion. In the process of evolution, the fitness is improved by the modulation in the connection weights among neurons.