Hebbian Learning Rule Restraining Catastrophic Forgetting in Pulse Neural Network

Abstract

In this paper, a Hebbian learning rule restraining “catastrophic forgetting” is proposed on pulse neural network (PNN) with leaky integrate-and-fire neurons. The strong point of this learning rule is that a learning of new pattern does not destroy past ones, and that an efficient use of synapses is enabled. First, in order to consider the function of the learning rule, a fundamental experiment is made. Next, to compare the performance between the proposed learning rule and conventional ones on the application, simulation experiments are examined using autonomous behavior robots which are forced to learn concurrently two different environments. The results of the experiments show that the proposed learning rule clearly restrains “catastrophic forgetting” and enables working of more efficient than conventional PNN learning.