Hierarchical Fusion Evolving Spiking Neural Network for Adaptive Learning

A majority of machine learning (ML) approaches functions in offline or batch modes, which limits their application to adaptive environments. Thus, developing algorithms that work in adaptive and dynamic environments is the subject of ongoing research. Such algorithms require to learn not only from new samples (online learning), but also from novel and unseen before knowledge. Here, we introduce the term evolving learning (EL) to refer to learning from new knowledge and unseen-before classes without needing to re-train models as in traditional ML methods. To achieve the goal of EL, we adopt a biologically-inspired paradigm to build a highly adaptive supervised learning algorithm based on two brain-like information processing: divide-and-conquer and hierarchical abstraction. Furthermore, our proposed algorithm, which we named it as Hierarchical Fusion Evolving Spiking Neural Network (HFSNN), uses a dynamical and biologically inspired spiking neural network (SNN) with the optimized neural model. HFSNN does not impose any limitation on the data regarding the number of classes or the way of feeding the data to the model. Our testing results show a proof-of-concept of HFSNN learning in offline, online and evolving learning mods and establish for future applications for EL.