Neuron Model

Neuron models are simplified mathematical representations of biological neurons, aiming to capture their essential computational properties for building artificial neural networks. Current research focuses on developing more biologically plausible models, such as spiking neural networks (SNNs) with various neuron types (e.g., Leaky Integrate-and-Fire, Izhikevich), and improving training methods beyond traditional backpropagation, including evolutionary algorithms and surrogate gradient techniques. These advancements are crucial for creating energy-efficient, high-performance artificial intelligence systems and for gaining deeper insights into the workings of the brain itself, with applications ranging from neuromorphic computing to robotic control and signal processing.