Assessing the Information Content of Individual Spikes in Population-Level Models of Neural Spiking Activity

TL;DR

This paper evaluates the information content of individual neural spikes using clusterless decoding algorithms and information-theoretic metrics, revealing how spike amplitude and prior information influence neural coding insights.

Contribution

It introduces an analysis of information content in spikes within clusterless models, highlighting the role of prior information and spike amplitude in neural data interpretation.

Findings

Low-amplitude spikes provide less information in isolation.

Considering prior spikes, all spikes convey similar information.

Clusterless models effectively capture neural coding mechanisms.

Abstract

In the last decade, there have been major advances in clusterless decoding algorithms for neural data analysis. These algorithms use the theory of marked point processes to describe the joint activity of many neurons simultaneously, without the need for spike sorting. In this study, we examine information-theoretic metrics to analyze the information extracted from each observed spike under such clusterless models. In an analysis of spatial coding in the rat hippocampus, we compared the entropy reduction between spike-sorted and clusterless models for both individual spikes observed in isolation and when the prior information from all previously observed spikes is accounted for. Our analysis demonstrates that low-amplitude spikes, which are difficult to cluster and often left out of spike sorting, provide reduced information compared to sortable, high-amplitude spikes when considered in isolation, but the two provide similar levels of information when considering all the prior information available from past spiking. These findings demonstrate the value of combining information measures with state-space modeling and yield new insights into the underlying mechanisms of neural computation.