A point process approach for the classification of noisy calcium imaging data

We study noisy calcium imaging data, with a focus on the classification of spike traces. As raw traces obscure the true temporal structure of neuron's activity, we performed a tuned filtering of the calcium concentration using two methods: a biophysical model and a kernel mapping. The former characterizes spike trains related to a particular triggering event, while the latter filters out the signal and refines the selection of the underlying neuronal response. Transitioning from traditional time series analysis to point process theory, the study explores spike-time distance metrics and point pattern prototypes to describe repeated observations. We assume that the analyzed neuron's firing events, i.e. spike occurrences, are temporal point process events. In particular, the study aims to categorize 47 point patterns by depth, assuming the similarity of spike occurrences within specific depth categories. The results highlight the pivotal roles of depth and stimuli in discerning diverse temporal structures of neuron firing events, confirming the point process approach based on prototype analysis is largely useful in the classification of spike traces.