Spike Sorting

Spike sorting is the crucial process of identifying and separating the electrical signals of individual neurons from multi-channel extracellular recordings, a critical step in neuroscience research and brain-computer interfaces (BCIs). Current research emphasizes developing faster, more accurate, and adaptable algorithms, often employing deep learning neural networks, neuromorphic models, or contrastive learning approaches to handle the increasing volume and complexity of data from high-density probes. These advancements aim to improve the reliability and efficiency of spike sorting, enabling more precise analysis of neural activity and ultimately facilitating breakthroughs in understanding brain function and developing advanced BCIs.