Spike Based Backpropagation

Spike-based backpropagation aims to develop learning algorithms for spiking neural networks (SNNs) that mimic the brain's efficiency and biological plausibility, overcoming limitations of current training methods. Research focuses on improving the accuracy and speed of SNN training through novel neuron models (e.g., KLIF), neuroevolutionary approaches for architecture design, and refined surrogate gradient methods for backpropagation. These advancements hold significant promise for creating energy-efficient artificial intelligence systems, particularly for applications like embedded systems and neuromorphic computing, where low power consumption is crucial.