Spiking Neural Networks: A Computational Paradigm for Neuromorphic Computing

Abstract

The third generation of neural networks is called Spiking Neural Networks (SNNs) and presents an energy-efficient and physiologically realistic alternative to the traditional Artificial Neural Networks (ANNs). This review discusses the latest advances in SNN models, learning algorithms, and their implementation on neuromorphic hardware. Besides reinforcement learning techniques such as REINFORCE, supervised techniques, such as SpikeProp and unsupervised Spike-Timing-Dependent Plasticity (STDP), are considered in the context of reward-based learning. The performance of the neurological systems technologies, including the IBM TrueNorth, the FPGA-based, and the Intel Loihi, is checked. Despite significant improvement in parallel processing and energy usage in these systems, there are still problems with accuracy of the synapses, training complexity, and scalability. FPGA-based systems are promising in real-time processing, though they have programmability issues that limit their deployment. The article outlines the key limitation of SNN models, namely their inability to handle large datasets and difficult learning tasks. To advance the performance of neuromorphic systems, research is underway on hybrid SNN-ANN models and memristor-based synaptic storage. This review is aimed at proving that SNNs have potential in such areas as computer vision, robotics, and speech recognition, and their role in overcoming the barrier between artificial and biological neural systems.