Spike Representation

Spike representation is a crucial aspect of spiking neural networks (SNNs), focusing on efficiently encoding information as sequences of neural spikes for energy-efficient computation. Current research emphasizes developing novel coding schemes, such as gated attention mechanisms, to improve the power and accuracy of SNNs, alongside innovative training methods like spike accumulation forwarding and differentiation on spike representation that address the inherent challenges of training these networks. These advancements are driving progress in applications ranging from low-power image processing and retinal prostheses to improved analysis of neural data like EEG signals, highlighting the importance of efficient spike representation for both neuromorphic computing and neuroscience.