A Model-Free Method to Quantify Memory Utilization in Neural Point Processes

Quantifying the predictive capacity of a neural system, intended as the capability to store information and actively use it for dynamic system evolution, is a key component of neural information processing. Information storage (IS), the main measure quantifying the active utilization of memory in a dynamic system, is only defined for discrete-time processes. While recent theoretical work laid the foundations for the continuous-time analysis of the predictive capacity stored in a process, methods for the effective computation of the related measures are needed to favor widespread utilization on neural data. This work introduces a method for the model-free estimation of the so-called memory utilization rate (MUR), the continuous-time counterpart of the IS, specifically designed to quantify the predictive capacity stored in neural point processes. The method employs nearest-neighbor entropy estimation applied to the inter-spike intervals measured from point-process realizations to quantify the extent of memory used by a spike train. An empirical procedure based on surrogate data is implemented to compensate the estimation bias and detect statistically significant levels of memory. The method is validated in simulated Poisson processes and in realistic models of coupled cortical dynamics and heartbeat dynamics. It is then applied to real spike trains reflecting central and autonomic nervous system activities: in spontaneously growing cortical neuron cultures, the MUR detected increasing memory utilization across maturation stages, associated to emergent bursting synchronized activity; in the study of the neuro-autonomic modulation of human heartbeats, the MUR reflected the sympathetic activation occurring with postural but not with mental stress. The proposed approach offers a computationally reliable tool to analyze spike train data in computational neuroscience and physiology.