An Adaptive Learning Algorithm Based on Spiking Neural Network for Global Optimization

The optimal computing ability of spiking neural networks (SNNs) mainly depends on the connection weights of their synapses and the thresholds that control the spiking. In order to realize the optimization calculation of different objective functions, it is necessary to modify the connection weights adaptively and make the thresholds dynamically self-learning. However, it is very difficult to construct an adaptive learning algorithm for spiking neural networks due to the discontinuity of neuron spike sending process, which is also a fatal problem in this field. In this paper, an efficient adaptive learning algorithm for spiking neural networks is proposed, which adjusts the weights of synaptic connections by a learning factor adaptively and adjusts the probability of spike sending by the self-organizing learning method of the dynamic threshold, so as to achieve the goal of automatic global search optimization. The algorithm is applied to the learning task of global optimization, and the experimental results show that this algorithm has good stability and learning ability, and is effective in dealing with complex multi-objective optimization problems of spatiotemporal spike mode. Moreover, the proposed framework explicitly leverages problem and model symmetries. In Traveling Salesman Problems, distance symmetry (d(i, j) = d(j, i)) and tour permutation symmetry are preserved by our spike-train-based similarity and energy updates, which do not depend on node labels. Together with the homogeneous neuron dynamics and balanced excitatory–inhibitory populations, these symmetry-aware properties reduce the effective search space and enhance the convergence stability.