Leaky Integrate and Fire

Leaky Integrate-and-Fire (LIF) neurons are a fundamental computational model in spiking neural networks (SNNs), aiming to mimic the behavior of biological neurons for energy-efficient computation. Current research focuses on improving LIF neuron models, including variations like adaptive, two-compartment, and gated LIF neurons, and developing efficient training algorithms such as online learning and surrogate gradient methods to overcome challenges associated with backpropagation. These advancements are driving progress in neuromorphic computing, enabling more efficient and powerful SNNs for applications ranging from embedded machine learning to modeling biological systems like tactile afferents.