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

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. To assess their quality, we previously proposed a goodness-of-fit technique for markedpoint process models based on time-rescaling, which for a correct model, produces a set of uniform samples over a random region of space. However, assessing uniformity over such a region can be challenging, especially in high dimensions.Here, we 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. We discuss properties of these transformations and demonstrate aspects of model fit captured by each transformation. We also compare multiple uniformity tests to determine their power to identify lack-of-fit in the rescaled data. We demonstrate an application of these transformations and Ground Intensity

Bayesian Decoder Models with a Discriminative Observation Process

“…The auto-encoder model is defined by where, are the model free parameters – to avoid model identifiability issue, we set a 1 to 1. Here, we assume the history term for the conditional intensity of spoken words can be defined by p k and q k , that corresponds to the number of spoken words from the participant and their companion respectively, over the last 400 milliseconds [8, 50]. The processing interval Δ is set to 50 milliseconds; small enough that the probability of more than one word inside each interval is negligible [15].…”