In this paper, a decentralized Greedy Assignment Learning solution framework using a Spiking neural network (de-GALS) is proposed for solving a Perimeter Defense Problem (PDP). A typical PDP scenario considers defenders protecting the perimeter of a convex territory from intruders. The region between the perimeter and the sensing ranges of defenders is divided into two different layers namely a sensing layer and a capture layer respectively. These layers are further divided into multiple angular segments. The layer closest to the perimeter is termed the capture layer in which the defenders operate and capture the intruders. The next layer is the sensing layer in which the intruders’ arrivals are sensed based on the defender’s sensors. The spatiotemporal movements of the defenders and intruders are converted into spikes and given as input to a Spiking Neural Network (SNN). In the SNN, the segments in the capture layer are assigned to a defender in a decentralized fashion to capture the intruders before they enter the territory. The SNN is trained in a supervised manner where the expert assignments are generated greedily based on the location of a defender. Based on the performance studies the proposed de-GALS framework shows better performance than other existing state-of-the-art solutions for PDP with the added advantage of requiring a less computational greedy approach for generating the expert data.