37 Assessing the information content of individual spikes in population level models of neural spiking activity

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In the last decade, there have been major advances in decoding from marked point process models that describe the joint activity of many neurons simultaneously, without the need for spike sorting. In this study, we examine entropy-based metrics to analyze the information that is extracted from each observed spike under such clusterless models. In an analysis of spatial coding in rat hippocampus, we compared the entropy reduction between spike sorted and clusterless models both for 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 our entropy measures and yield new insights into the underlying mechanisms of neural computation.

Marked point process models

clusterless decoding

Information measures for spike trains