Fast Online Deconvolution of Calcium Imaging Data

Abstract

Fluorescent calcium indicators are a popular means for observing the spiking activity of large neuronal populations, but extracting the activity of each neuron from raw fluorescence calcium imaging data is a nontrivial problem. We present a fast online active set method to solve this sparse non-negative deconvolution problem. Importantly, the algorithm progresses through each time series sequentially from beginning to end, thus enabling real-time online estimation of neural activity during the imaging session. Our algorithm is a generalization of the pool adjacent violators algorithm (PAVA) for isotonic regression and inherits its linear-time computational complexity. We gain remarkable increases in processing speed: more than one order of magnitude compared to currently employed state of the art convex solvers relying on interior point methods. Unlike these approaches, our method can exploit warm starts; therefore optimizing model hyperparameters only requires a handful of passes through the data. A minor modification can further improve the quality of activity inference by imposing a constraint on the minimum spike size. The algorithm enables real-time simultaneous deconvolution of $O(10^5)$ traces of whole-brain larval zebrafish imaging data on a laptop.

Extended version that significantly elaborates on the conference proceeding that appeared at NIPS 2016

References in corpus (3)

• Fast non-negative deconvolution for spike train inference from population calcium imaging
• CVXPY: A Python-Embedded Modeling Language for Convex Optimization
• A Bayesian approach for inferring neuronal connectivity from calcium fluorescent imaging data

Cited by in corpus (17)

• Metastable dynamics of neural circuits and networks
• Covariance-Free Sparse Bayesian Learning
• Correcting motion induced fluorescence artifacts in two-channel neural imaging
• Sparse and Smooth Signal Estimation: Convexification of L0 Formulations
• Efficient and accurate extraction of in vivo calcium signals from microendoscopic video data
• A zero-inflated gamma model for deconvolved calcium imaging traces
• Scale-free correlations in the dynamics of a small (N ~ 10000) cortical network
• Hemodynamic Deconvolution Demystified: Sparsity-Driven Regularization at Work
• Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time
• Short-and-Sparse Deconvolution -- A Geometric Approach
• Discrete flow posteriors for variational inference in discrete dynamical systems
• The Virtual Patch Clamp: Imputing C. elegans Membrane Potentials from Calcium Imaging
• Quantifying uncertainty in spikes estimated from calcium imaging data
• Asymptotics of Ridge Regression in Convolutional Models
• Inferring Mesoscale Models of Neural Computation
• Time-varying ${\ell }_0$ optimization for Spike Inference from Multi-Trial Calcium Recordings
• Calcium imaging and analysis of the mouse hippocampus or neocortex using miniature microendoscopes