Training Spiking Neural Networks with Metaheuristic Algorithms

Abstract

Taking inspiration from the brain, spiking neural networks (SNNs) have been proposed to understand and diminish the gap between machine learning and neuromorphic computing. Supervised learning is the most commonly used learning algorithm in traditional ANNs. However, directly training SNNs with backpropagation-based supervised learning methods is challenging due to the discontinuous and non-differentiable nature of the spiking neuron. To overcome these problems, this paper proposes a novel metaheuristic-based supervised learning method for SNNs by adapting the temporal error function. We investigated seven well-known metaheuristic algorithms called Harmony Search (HS), Cuckoo Search (CS), Differential Evolution (DE), Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Grammatical Evolution (GE) as search methods for carrying out network training. Relative target firing times were used instead of fixed and predetermined ones, making the computation of the error function simpler. The performance of our proposed approach was evaluated using five benchmark databases collected in the UCI Machine Learning Repository. The experimental results showed that the proposed algorithm had a competitive advantage in solving the four classification benchmark datasets compared to the other experimental algorithms, with accuracy levels of 0.9858, 0.9768, 0.7752, and 0.6871 for iris, cancer, diabetes, and liver datasets, respectively. Among the seven metaheuristic algorithms, CS reported the best performance.