Spike Based Learning

Spike-based learning aims to develop neural networks that process information using brief electrical pulses (spikes), mirroring biological neurons, for improved energy efficiency and computational speed. Current research focuses on developing effective training algorithms, such as variations of backpropagation adapted for spike-based systems and biologically-inspired rules like STDP, often implemented in recurrent convolutional networks. These efforts are driven by the potential for creating more efficient and powerful neuromorphic computing systems for applications like object detection and image classification, particularly in resource-constrained environments.