Topic: extracellular neural data

The increasing complexity and volume of extracellular neural data generated by modern silicon-based probes present significant storage, transmission, and analysis challenges. Spike sorting algorithms have been developed to cope with this data deluge, but innovative approaches are required to compress the data while maintaining information fidelity efficiently.This project explores the application of deep learning techniques for compressing extracellular neural data, leveraging the hierarchical representations learned by convolutional neural networks to achieve efficient compression without com