Unsupervised Discovery of Temporal Sequences in High-Dimensional Datasets

Explore an advanced tutorial on unsupervised discovery of temporal sequences in high-dimensional datasets, focusing on a novel tool called seqNMF. Delve into the challenges of identifying interpretable, low-dimensional features in large-scale neural recordings and learn how seqNMF extends convolutional non-negative matrix factorization techniques to extract significant sequences from neural data. Discover the tool's application to various neural and behavioral datasets, and gain hands-on experience with demo code. Understand the cross-validation procedures for assessing factor significance, choosing optimal parameters, and validating results on held-out data. Engage with practical examples, simulations, and real-world applications in neuroscience, equipping yourself with cutting-edge techniques for analyzing complex temporal patterns in high-dimensional data.