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

TL;DR

This paper introduces a point process methodology for classifying noisy calcium imaging data by filtering signals and analyzing spike timing, demonstrating its effectiveness in categorizing neuronal activity based on depth and stimuli.

Contribution

It presents a novel approach combining biophysical modeling and kernel mapping with point process theory for classifying neuronal spike data.

Findings

Depth and stimuli significantly influence spike pattern classification.

Point process prototype analysis effectively distinguishes neuronal firing patterns.

Filtering methods improve spike detection accuracy.

Abstract

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.