Can climbers take the uphill slope again?: a fitness landscape on weight space of an application using spiking neurons under rate coding

Abstract

We simulate an associative memory model using spiking neurons instead of McCulloch-Pitts neurons. To store a set of patterns, we employ a Hebbian-like learning algorithm. The learning behavior, however, is somewhat of a different one of the traditional Hopfield model. To study the difference we explore the fitness landscape defined on synaptic weights space when they evolve searching for the optimal learning.