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Gaussian Process-Based Filtering for Efficient Neural Decoding

This research presents a novel approach utilizing Gaussian Processes (GPs) for neural decoding and observation mapping. By employing the Unscented Kalman Filter (UKF), we address the challenges of non-linear observation mapping, enhancing decoding performance. Our method demonstrates significant improvements in fidelity, using only one-fifth of the training data to achieve better generative models. The results indicate a robust framework for neural decoding that efficiently captures complex dynamics, positioning it as a promising tool in the field of neuroinformatics.

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Gaussian Process-Based Filtering for Efficient Neural Decoding

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  1. Gaussian Process Based Filtering for Neural Decoding Karthik Lakshmanan, Humphrey Hu, Arun Venkatraman April 24, 2013 University of Pittsburgh Sony Pictures http://cs.cmu.edu/~arunvenk/academics/neural/

  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. Results & Conclusion • Improved decoding & produced a higher fidelity generative (observation) model (trained on 1/5 of training data)

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