Spiking Neural Networks (SNN) - in 5 Minutes

Dive into the world of Spiking Neural Networks (SNNs) with this quick 5-minute overview. SNNs mimic biological neural networks more closely than traditional artificial neural networks by using discrete spike events to transmit information. This approach introduces the concept of time, allowing SNNs to excel in processing temporal data. We'll explore key components like neuron models (e.g., Leaky Integrate-and-Fire), how data is encoded into spikes, and how learning occurs through mechanisms like Spike-Timing-Dependent Plasticity. Practical examples using Python and PyTorch will show how to build and train SNNs, highlighting their potential in fields like neuromorphic computing, where energy efficiency and real-time processing are crucial. Get a taste of how SNNs might revolutionize AI with their bio-inspired approach.