Spike Train

Spike trains, sequences of neuronal firings, are crucial for understanding neural information processing and building energy-efficient neuromorphic computing systems. Current research focuses on developing efficient algorithms for spike train classification and decoding, often employing spiking neural networks (SNNs) with architectures like recurrent SNNs and convolutional SNNs, and leveraging techniques such as surrogate gradient descent and spike encoding methods. These advancements are significant for both neuroscience, enabling better understanding of brain function, and engineering, paving the way for low-power AI applications in areas like brain-computer interfaces and sensory processing.