Multiscale relevance and informative encoding in neuronal spike trains

TL;DR

This paper introduces the multiscale relevance (MSR), a new metric for analyzing neuronal spike trains that captures activity variability across multiple time scales without prior assumptions, aiding in identifying neurons with significant information content.

Contribution

The paper proposes the MSR metric, a non-parametric measure that characterizes neural activity across time scales and helps identify neurons encoding meaningful information.

Findings

Neurons with high MSR encode spatial and directional information effectively.

Low MSR neurons tend to have low mutual information and sparsity.

MSR can rank neurons by their informational relevance without prior covariates.

Abstract

Neuronal responses to complex stimuli and tasks can encompass a wide range of time scales. Understanding these responses requires measures that characterize how the information on these response patterns are represented across multiple temporal resolutions. In this paper we propose a metric -- which we call multiscale relevance (MSR) -- to capture the dynamical variability of the activity of single neurons across different time scales. The MSR is a non-parametric, fully featureless indicator in that it uses only the time stamps of the firing activity without resorting to any a priori covariate or invoking any specific structure in the tuning curve for neural activity. When applied to neural data from the mEC and from the ADn and PoS regions of freely-behaving rodents, we found that neurons having low MSR tend to have low mutual information and low firing sparsity across the correlates that are believed to be encoded by the region of the brain where the recordings were made. In addition, neurons with high MSR contain significant information on spatial navigation and allow to decode spatial position or head direction as efficiently as those neurons whose firing activity has high mutual information with the covariate to be decoded and significantly better than the set of neurons with high local variations in their interspike intervals. Given these results, we propose that the MSR can be used as a measure to rank and select neurons for their information content without the need to appeal to any a priori covariate.