Spiking Neural Networks (SNNs) are a type of artificial neural network that mimics the way biological neurons work by incorporating a time-dependent behavior into the neurons. This behavior allows neurons to communicate with each other and process information in a more biologically-realistic way. Instead of relying solely on activation values like traditional neural networks, SNNs use spikes, or electrical impulses, to send and receive information. This makes them better at modeling complex, dynamic relationships and can help improve the performance of machine learning applications.