Predicting Spike Trains from PMd to M1 Using Discrete Time Rescaling Targeted GLM

The computational model for spike train prediction with inputs from other related cerebral cortices is important in revealing the underlying connection among different cortical areas. To evaluate goodness-of-fit of the model, the time rescaling Kolmogorov-Smirnov (KS) statistic is usually applied, of which the calculation is separated from optimization procedure of the model. If the KS statistic could be embedded into objective function of the optimization procedure, precision of the firing probability series generated by the model would be increased directly. This paper presents a linear-nonlinear-Poisson cascade framework for prediction of spike trains, whose objective function is changed from maximizing log-likelihood of the spike trains to minimizing the penalization of discrete time rescaling KS statistic to eliminate the separation between optimization and evaluation of the model. We apply our model on the task of predicting firing probability of neurons from primary motor cortex with spike trains from dorsal premotor cortex as input, which are two cerebral cortices associated with movements planning and executing. The experimental results show that by introducing the goodness-of-fit metric into the objective function, results of the model will gain a significant improvement, which outperforms the state of the art.

I. Introduction

The development of multiple electrode neural spike recording techniques [1] has facilitated many statistical models to characterize the spiking activities among various parts of cerebral cortices, which in turn enables researchers to understand the brain’s functional circuits more in depth. Building models for neural spikes among different cortical regions has already contributed to understanding the functionality relationship among various types of neurons [2]–[4], assessing their functional connectivity [5], [6], deciphering spiking signals within the brain [7]–[9], performing pattern recognition with spiking neural networks [10], and even designing neural prostheses [11] in order to compensate the loss of functionality caused by brain injury.