Optimization via efficient learning in CNNs: Cognitively-motivated temporal discount functions in SRNNs

Abstract

Cellular Neural Networks (CNNs) are universal computing machines embodying basic computational principles of cortical tissues. Simultaneous Recurrent Neural Networks (SRNNs) have shown clear advantages in solving complex optimization and decision making problems. Based on biological intuition, we introduce temporal discount functions in training SRNNs as a generalization of the adaptive learning rate concept. The proposed procedure results in drastic, 3-5-fold acceleration of learning, demonstrated through the maze navigation problem.