Training an approximate logic dendritic neuron model using social learning particle swarm optimization algorithm

With the rapid development of artificial neural networks, recent studies have shown that dendrites play a vital role in neural computations. In this study, we propose a dendritic neuron model called the approximate logic dendritic neuron model (ALDNM) to solve classification problems. The ALDNM can be divided into four layers: the synaptic layer, the dendritic layer, the membrane layer, and the soma body. Considering the limitation of the back-propagation (BP) algorithm, we employ a heuristic optimization called the social learning particle swarm optimization algorithm (SL-PSO) to train the ALDNM. In order to investigate the effectiveness of SL-PSO for training the ALDNM, we compare this training method with BP and four other typical heuristic optimization methods. Moreover, the proposed ALDNM is also compared with seven classifiers to verify its performance. The experimental results and statistical analysis on four classification problems indicate that the proposed ALDNM trained by SL-PSO can provide a competitive performance for solving the classification problems. It is worth emphasizing that the structure of the trained ALDNM can be greatly simplified owing to the unique pruning operations. Furthermore, the simplified ALDNM for a specific problem can be converted into a corresponding logic circuit classifier for a fast classification.