Show notes
Welcome back to Neurotech Pub!In this episode, Vikash Gilja reprises his role as Vikash Gilja. We are also joined by Konrad Kording, Chethan Pandarinath, and Carsen Stringer. We talk about how dimensionality reduction is used to better understand large scale neural recordings. This episode is fairly technical, but it contains many great references if you are interested in learning more. We open with a brief explainer video by Paradromics’ own Aditya Singh.Check out full video with transcript here: https://www.paradromics.com/podcast/neurotech-pub-episode-4-trading-spaces-dimensionality-reduction-for-neural-recordings00:40 | Dimensionality Intro04:42 | Podcast Start07:50 | Janelia Research Campus08:56 | Translational Neuroengineering Lab09:35 | Stanford Neural Prosthetics Translational Lab10:10 | Shenoy Lab12:00 | Deep Brain Stimulation12:57 | Chethan’s work on retinal prosthetics15:00 | Immunology15:20 | Jonathan Ruben15:30 | Byron Yu15:41 | Gatsby Computational Neuroscience Unit18:00 | Joshua Tenenbaum18:30 | Kording Lab at UPenn18:46 | Neuromatch Academy19:47 | Neuromatch Academy Q&A21:21 | Dimensionality reduction for neural recordings26:22 | The Curse of Dimensionality30:11 | Principal Component Analysis32:20 | Neural Firing as a Poisson Process33:13 | Shared Variance Component Analysis35:18 | Cross validation in large scale recording38:29 | A theory of multineuronal dimensionality39:10 | Random projections explained with visuals42:24 | Correcting a reductionist bias48:30 | Noise Correlations49:35 | More on Noise Correlations57:40 | LFADS01:01:51 | What is a stationary process?01:06:02 | Inferring single-trial neural population dynamics01:06:46 | Task Specificity01:07:28 | Lee Miller01:08:18 | “I don’t know, I might be wrong”01:13:16 | Neural Constraints on Learning01:15:00 | A recent exciting paper from Yu and Batista Labs01:19:01 | Hume on CausationWant more? Follow Paradromics & Neurotech Pub on Twitter Follow Matt A, Konrad Kording, Chethan Pandarinath, and Carsen Stringer on Twitter.
