There are good theoretical reasons to believe that neurons learn to predict their input, but fewer experimental tests of this hypothesis. The mentor and co-mentor on this project have developed new methods for assessing the predictive capabilities of a large number of neurons from data using both information theoretic and a Bayesian framework. These methods have not yet been optimized and integrated into an existing, ever-growing codebase that will, upon release, allow other groups to easily assess the predictive capabilities of their neural populations.
One of the metrics for assessing stimulus prediction is based on the predictive information, the shared information between present neural response and future stimulus. The student on this project is expected to be familiar with Python, Matlab, and TensorFlow/Keras, and either familiar with or eager to learn new techniques for mutual information estimation in the undersampled limit. He or she will implement existing state-of-the-art algorithms for such estimation and add such algorithms to the existing codebase.
The result of this project will be a state-of-the-art compilation of predictive information estimation methods.
Mentor: Sarah Marzen @smarzen
Co-mentor: Joost le Feber