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There is now a CONTENT FREEZE for Mercury while we switch to a new platform. It began on Friday, March 10 at 6pm and will end on Wednesday, March 15 at noon. No new content can be created during this time, but all material in the system as of the beginning of the freeze will be migrated to the new platform, including users and groups. Functionally the new site is identical to the old one. webteam@gatech.edu
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"Finding Low-dimensional Structure in Large-scale Neural Recordings"
Eva Dyer, Ph.D.
Assistant Professor
Wallace H. Coulter Department of Biomedical Engineering
Georgia Tech
Improvements in neural recording technologies have rapidly increased the number of neurons that it is now possible to record from. Along with these improvements, analyses of neural information processing are moving from single neuron to population-level analyses. One promising approach for understanding information processing across large populations of neurons is to use methods for dimensionality reduction; such approaches aim to find low-dimensional structure in the joint activity of many neurons over time. In this talk, I will describe my lab's efforts to learn low-dimensional structure present in large-scale neural recordings, both from electrophysiology recordings in motor cortex and from two-photon calcium movies in primary visual cortex. Our findings suggest that dimensionality reduction techniques can be used to pull out structure from neural activity to solve a range of decoding and classification problems.
