|Ph.D Student||Krumin Michael|
|Subject||Correlation-Distortion Based Control and Analysis of|
Neural Spike Trains
|Department||Department of Biomedical Engineering||Supervisor||Professor Shy Shoham|
|Full Thesis text|
During the last couple of decades there is emerging evidence for the importance of correlated firing in the activity of different neural systems including the retina and the primary motor cortex. Quantitative descriptions and models of these correlations are needed in order to further clarify their role and test various related hypotheses. In this study we analyze the transformation of noise and signal correlations as they propagate in feed-forward and feedback multivariate neural cascade models. The derived formulas are used to introduce a “correlation distortion” framework for systematically controlling or analyzing neural spike trains (mathematical point processes) based on their correlation structure. The framework is shown to enable the generation of synthetic spike trains with predefined correlation structure, correlation-based parametric Granger causality analysis of information flow in a neural network, and the “blind” identification of the functional properties of neurons in the visual system.
Some of the most fundamental tools for the identification of engineering systems rely on the use of correlations; our work illustrates that tools for identifying spike train models from their correlations could form a welcome bridge between ‘classical’ signal processing and the field of neural spike train analysis.