1. Gaussian Process Based Filtering for Neural Decoding
Karthik Lakshmanan, Humphrey Hu, Arun Venkatraman
April 24, 2013
University of Pittsburgh
Sony Pictures

2. Setup & Motivation

3. Proposed Method
Model non-linear observation mapping with Gaussian Processes (GPs)
Need to use Unscented Kalman Filter (UKF)
However, this can be slow to evaluate…

4. Dimensionality Reduction

5. Trajectory Reconstruction Neural Reconstruction
Results & Conclusion
Improved decoding & produced a higher fidelity generative (observation) model
Trajectory Reconstruction
Final Cursor Position
Neural Reconstruction
% Improvement of GP-UKF over KF (both non-dim-reduced)
33.6%
42.8%
43.4%
% Improvement of GP-UKF (w/PCA) over KF (non-dim-reduced)
22.2%
16.8% -
% Improvement of GP-UKF (w/FA) over KF (non-dim-reduced)
-0.80%
-7.20%
(trained on 1/5 of training data)