To Deconvolve, or Not to Deconvolve: Inferences of Neuronal Activities using Calcium Imaging Data

03/03/2021

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With the increasing popularity of calcium imaging data in neuroscience research, methods for analyzing calcium trace data are critical to address various questions. The observed calcium traces are either analyzed directly or deconvolved to spike trains to infer neuronal activities. When both approaches are applicable, it is unclear whether deconvolving calcium traces is a necessary step. In this article, we compare the performance of using calcium traces or their deconvolved spike trains for three common analyses: clustering, principal component analysis (PCA), and population decoding. Our simulations and applications to real data suggest that the estimated spike data outperform calcium trace data for both clustering and PCA. Although calcium trace data show higher predictability than spike data at each time point, spike history or cumulative spike counts is comparable to or better than calcium traces in population decoding.

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To Deconvolve, or Not to Deconvolve: Inferences of Neuronal Activities using Calcium Imaging Data

On the Multiway Principal Component Analysis

Supervised learning sets benchmark for robust spike detection from calcium imaging signals

Fast Nonconvex Deconvolution of Calcium Imaging Data

Estimation of the number of spikes using a generalized spike population model and application to RNA-seq data

Computationally efficient model selection for joint spikes and waveforms decoding

Bayes Risk Consistency of Nonparametric Classification Rules for Spike Trains Data

An uncertainty principle for neural coding: Conjugate representations of position and velocity are mapped onto firing rates and co-firing rates of neural spike trains

To Deconvolve, or Not to Deconvolve: Inferences of Neuronal Activities using Calcium Imaging Data

Related Research

On the Multiway Principal Component Analysis

Supervised learning sets benchmark for robust spike detection from calcium imaging signals

Fast Nonconvex Deconvolution of Calcium Imaging Data

Estimation of the number of spikes using a generalized spike population model and application to RNA-seq data

Computationally efficient model selection for joint spikes and waveforms decoding

Bayes Risk Consistency of Nonparametric Classification Rules for Spike Trains Data

An uncertainty principle for neural coding: Conjugate representations of position and velocity are mapped onto firing rates and co-firing rates of neural spike trains