have an interactive transcript feature enabled, which appears below the video when playing. Viewers can search for keywords in the video or click on any word in the transcript to jump to that point in the video. When searching, a dark bar with white vertical lines appears below the video frame. Each white line is an occurrence of the searched term and can be clicked on to jump to that spot in the video.
LEARNINGUnsupervised discovery of temporal sequences in high-dimensional datasets (1:14:24)
- Computational Tutorials
The ability to identify interpretable, low-dimensional features that capture the dynamics of large-scale neural recordings is a major challenge in neuroscience. Dynamics that include repeated temporal patterns (which we call sequences), are not succinctly captured by traditional dimensionality reduction techniques such as principal components analysis (PCA) and non-negative matrix factorization (NMF). The presence of neural sequences is commonly demonstrated using visual display of trial-averaged firing rates. However, the field suffers from a lack of task-independent, unsupervised tools for consistently identifying sequences directly from neural data, and cross-validating these sequences on held-out data. This tutorial introduces a tool called seqNMF, for unsupervised discovery of temporal sequences in high-dimensional datasets, which extends a convolutional NMF technique. It provides a framework for extracting sequences from a dataset, and is easily cross-validated to assess the significance of each extracted factor. This tutorial provides code to apply seqNMF to several neural and behavioral datasets, and provide demo code.
Taught by: Emily Mackevicius and Andrew Bahle, MIT
- Github: https://github.com/Feelab/seqNMF
- Biorxiv: https://www.biorxiv.org/content/early/2018/03/02/273128
- BCS tutorials materials: https://stellar.mit.edu/S/project/bcs-comp-tut/materia