Internal KCN: Assessing Goodness-of-Fit in Marked-Point Process Models of Neural Population Coding via Time and Rate Rescaling

Paper Link: Assessing Goodness-of-Fit in Marked Point Process Models of Neural Population Coding via Time and Rate Rescaling | Neural Computation | MIT Press

Abstract:

Marked-point process models have recently been used to capture the coding properties of neural populations from multi-unit electrophysiological recordings without spike sorting. These ‘clusterless’ models have been shown in some instances to better describe the firing properties of neural populations than collections of receptive field models for sorted neurons and to lead to better decoding results. A fundamental component of any statistical model is the ability to evaluate the quality of its fit to observed data. Recently methods for assessing the goodness-of-fit of Marked point process models have been lacking. In this presentation, I propose a set of new transformations both in time and in the space of spike waveform features, which generate events that are uniformly distributed in the new mark and time spaces. These transformations are scalable to multi-dimensional mark spaces and provide uniformly distributed samples in hypercubes, which are well suited for uniformity tests. I discuss properties of these transformations and demonstrate aspects of model fit captured by each transformation. I also compare multiple uniformity tests to determine their power to identify lack-of-fit in the rescaled data and demonstrate an application of these transformations and uniformity tests in a simulation study and real data.