Non Spiking

Non-spiking neural networks represent a significant area of research aiming to improve the energy efficiency and speed of deep learning by leveraging the sparse, event-driven nature of biological neurons. Current efforts focus on developing efficient training algorithms, such as Equilibrium Propagation, and novel neuron models like the Stochastic Parallelizable Spiking Neuron, to overcome limitations of traditional spiking neural network (SNN) training methods. These advancements are leading to improved performance in various applications, including image recognition and natural language processing, while also exploring the potential of specialized hardware like Intelligence Processing Units for accelerated training. The ultimate goal is to create high-performing, energy-efficient SNNs that rival or surpass the capabilities of their non-spiking counterparts.