Dynamic PCA for network feature extraction in multi-electrode recording of neurophysiological data in cortical substrate of pain

摘要

A novel multi-scale analysis of multi-electrode spike recording during heat pain stimulation in rats is applied to quantify non-stationary patterns of neuronal response. This approach would allow biological constraints to translate into multi-dimensional geometry. We then determine the optimal scale, resolution and density of a neuronal localization that best characterizes the cortical response. Within the optimal choices, we determine the inherent dimension of the locally linear principal component analysis (PCA) that approximates the non-linear geometric structure of data, and minimzes the reconstruction error within the prescribed bounds. When dimension is one, two, or three, our optimization algorithms determine the system of non-linear principal curves that best approximates the data.