🤖 AI Summary
It remains unclear whether the information content of individual spikes in population neural coding models—without spike sorting—depends on spike amplitude and prior spiking history. Method: Using multi-neuron marked point process data from rat hippocampus, we quantified the information carried by isolated spikes versus conditionally decoded spikes (i.e., incorporating preceding spike sequences), employing information-theoretic entropy measures, state-space modeling, and a clusterless decoding framework. Contribution/Results: While low-amplitude spikes convey significantly less information than high-amplitude ones when analyzed in isolation, their conditional information—when contextualized by recent spiking history—approaches or equals that of high-amplitude spikes. This is the first systematic demonstration that poorly clusterable spikes are not noise but encode critical temporal information. The findings provide theoretical justification and empirical support for abandoning traditional spike sorting and advancing continuous-feature-based neural decoding paradigms.
📝 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.