Spike Based

Spike-based neural networks (SNNs) aim to mimic the brain's energy-efficient, event-driven computation by representing information as sequences of spikes. Current research focuses on improving SNN performance through novel architectures like spike transformers and enhanced leaky integrate-and-fire neurons, as well as developing efficient training methods such as those based on implicit differentiation and evolutionary algorithms. This field is significant due to SNNs' potential for low-power applications in brain-computer interfaces, neuromorphic computing, and other areas requiring energy-efficient AI.