Heterogeneous Ensemble-Based Spike-Driven Few-Shot Online Learning - Yang, Shuangming; Linares-Barranco, Bernabe; Chen, Badong (Frontiers in Neuroscience, 2022)
Spiking neural networks (SNNs) are regarded as a promising candidate to deal with great challenges of current machine learning techniques, including the high energy consumption induced by deep neural networks. However, there is still a great gap between SNNs and the few-shot learning performance of artificial neural networks. Importantly, existing spike-based few-shot learning models do not target the robust learning based on spatiotemporal dynamics and superior machine learning theory. In this article, we propose a novel spike-based framework with entropy theory, namely heterogeneous ensemble-based spike-driven few-shot learning (HESFOL). The proposed HESFOL model uses the entropy theory to establish the gradient-based few-shot learning scheme in a recurrent SNN architecture. We examine the performance of HESFOL model based on the few-shot classification tasks using spiking patterns and Omniglot data set, as well as the few-shot motor control task using the end-effector. The experimental results show that the proposed HESFOL scheme can effectively improve the accuracy and the robustness of the spike-driven few-shot learning performance. More importantly, the proposed HESFOL model emphasizes the application of the modern entropy-based machine learning methods on the state-of-the-art spike-driven learning algorithms. Therefore, our study provides new perspectives for further integration of advanced entropy theory in machine learning to improve the learning performance of SNNs, which could be of great merit to applied developments with spike-based neuromorphic systems.