Spiking Neuron

Spiking neurons are biologically inspired computational models that transmit information via discrete events called spikes, offering potential advantages in energy efficiency and temporal processing compared to traditional artificial neurons. Current research focuses on developing efficient training algorithms for spiking neural networks (SNNs), exploring various architectures like those based on convolutional networks, transformers, and recurrent neural networks (including Leaky Integrate-and-Fire and two-compartment models), and optimizing SNNs for specific applications such as robotic control, image generation, and signal processing. This research is significant because SNNs promise to enable more energy-efficient and biologically plausible artificial intelligence, with potential applications in resource-constrained devices and systems requiring real-time processing of temporal data.