Dimensionality reduction in neural data analysis – xcorr: comp neuro

It’s become commonplace to record from hundreds of neurons simultaneously. If past trends extrapolate, we might commonly record 10k neurons by 2030. What are we going to do with all this data…

It’s become commonplace to record from hundreds of neurons simultaneously. If past trends extrapolate, we might commonly record 10k neurons by 2030. What are we going to do with all this data? Neural dynamics tend to be lower-dimensional than the number of neurons recorded would suggest – Humphries (2020) reports 10X to 100X compression depending on the area and task. Humphries suggests, with the strong principle of neural dimensionality reduction, that dimensionality reduction can show us the true underlying signal embodied by the neural circuit. Even if we don’t fully believe in this…

saved by

• John E Bailey
• Marley Xiong
• Raffi Hotter
• Frederick Zhang

related reading

• Dimensionality reduction for large-scale neural recordings - PubMedpubmed.ncbi.nlm.nih.gov
• Estimating the dimensionality of the manifold underlying multi-electrode neural recordingsncbi.nlm.nih.gov
• Gaussian-Process Factor Analysis for Low-Dimensional Single-Trial Analysis of Neural Population Activity - PMCpmc.ncbi.nlm.nih.gov
• The Dimension Question: How High Does It Go?simonsfoundation.org
• Toy Models of Superpositiontransformer-circuits.pub
• 2205.08413.pdfarxiv.org
• Dimensionality Reduction for Neural RecordingsParadromicsparadromics.com
• Scaling Properties of Dimensionality Reduction for Neural Populations and Network Models | PLOS Computational Biologyjournals.plos.org
• Could a Neuroscientist Understand a Microprocessor? | PLOS Computational Biologyjournals.plos.org
• All things neural distribution alignment | neuralignnerdslab.github.io
• Neural data science: accelerating the experiment-analysis-theory cycle in large-scale neuroscience | bioRxivbiorxiv.org
• Visualizing Neural Networks with the Grand Tourdistill.pub