Daniel Greenidge will present his General Exam "Efficient decoding of large-scale neural population responses with Gaussian-process multiclass regression" on Friday, April 29, 2022 at 3:00 PM via Zoom.
Committee Members: Jonathan Pillow (advisor), Adji Bousso Dieng, Ben Raphael
Abstract:
Linear classifiers are a tool used by neuroscientists to quantify the information content of neural population codes and the limits imposed by correlations in neural activity. However, the most commonly used classification methods are prone to overfitting on the high-dimensional, noisy datasets produced by modern neuroimaging technologies. Here, we introduce a novel classifier (also called “decoder” in the neuroscience literature) to overcome these limitations. Our approach, the Gaussian process multi-class decoder (GPMD), is well-suited to decoding a continuous low-dimensional variable from high-dimensional population activity, and provides a platform for assessing the importance of correlations in neural population codes. The GPMD is a multinomial logistic regression model with a Gaussian Process prior over the decoding weights. We provide a spectral variational inference method for fitting the GPMD to data, which scales to datasets with hundreds or thousands of neurons and performs especially well in datasets with more neurons than trials. We apply the GPMD to recordings from primary visual cortex in three different species: monkey, ferret, and mouse. Our classifier achieves state-of-the-art accuracy on all three datasets, and substantially outperforms independent Bayesian decoding, showing that knowledge of the correlation structure is essential for optimal decoding in all three species. We then use our classifier to examine the problems of model mismatch, cortical microcolumn structure, and how neural coding strategies are modulated in the presence of noise.
Reading List:
Everyone is invited to attend the talk, and those faculty wishing to remain for the oral exam following are welcome to do so.