scholarly journals Correction: Inferring Neuronal Dynamics from Calcium Imaging Data Using Biophysical Models and Bayesian Inference

2016 ◽  
Vol 12 (3) ◽  
pp. e1004835 ◽  
Author(s):  
Keyword(s):  
Bayesian Inference ◽  
Calcium Imaging ◽  
Imaging Data ◽  
Neuronal Dynamics ◽  
Biophysical Models

scholarly journals Inferring Neuronal Dynamics from Calcium Imaging Data Using Biophysical Models and Bayesian Inference

2016 ◽  
Vol 12 (2) ◽  
pp. e1004736 ◽  
Author(s):  
Vahid Rahmati ◽  
Knut Kirmse ◽  
Dimitrije Marković ◽  
Knut Holthoff ◽  
Stefan J. Kiebel
Keyword(s):  
Bayesian Inference ◽  
Calcium Imaging ◽  
Imaging Data ◽  
Neuronal Dynamics ◽  
Biophysical Models

scholarly journals Automated cellular structure extraction in biological images with applications to calcium imaging data

2018 ◽  
Author(s):  
Gal Mishne ◽  
Ronald R. Coifman ◽  
Maria Lavzin ◽  
Jackie Schiller
Keyword(s):  
Calcium Imaging ◽  
Image Plane ◽  
Cellular Level ◽  
Full Potential ◽  
Imaging Data ◽  
Image Domain ◽  
Biological Images ◽  
Large Populations ◽  
In Vivo Calcium Imaging

AbstractRecent advances in experimental methods in neuroscience enable measuring in-vivo activity of large populations of neurons at cellular level resolution. To leverage the full potential of these complex datasets and analyze the dynamics of individual neurons, it is essential to extract high-resolution regions of interest, while addressing demixing of overlapping spatial components and denoising of the temporal signal of each neuron. In this paper, we propose a data-driven solution to these challenges, by representing the spatiotemporal volume as a graph in the image plane. Based on the spectral embedding of this graph calculated across trials, we propose a new clustering method, Local Selective Spectral Clustering, capable of handling overlapping clusters and disregarding clutter. We also present a new nonlinear mapping which recovers the structural map of the neurons and dendrites, and global video denoising. We demonstrate our approach on in-vivo calcium imaging of neurons and apical dendrites, automatically extracting complex structures in the image domain, and denoising and demixing their time-traces.

scholarly journals Community-based benchmarking improves spike rate inference from two-photon calcium imaging data

2018 ◽  
Vol 14 (5) ◽  
pp. e1006157 ◽  
Author(s):  
Philipp Berens ◽  
Jeremy Freeman ◽  
Thomas Deneux ◽  
Nikolay Chenkov ◽  
Thomas McColgan ◽  
...  
Keyword(s):  
Calcium Imaging ◽  
Spike Rate ◽  
Imaging Data ◽  
Community Based ◽  
Two Photon

Detecting neuronal activity from calcium imaging data using FRI methods (Conference Presentation)

2017 ◽  
Author(s):  
Stephanie Reynolds ◽  
Jon Oñativia ◽  
Simon R. Schultz ◽  
Pier Luigi Dragotti
Keyword(s):  
Neuronal Activity ◽  
Calcium Imaging ◽  
Imaging Data

scholarly journals Ultra-fast accurate reconstruction of spiking activity from calcium imaging data

2018 ◽  
Vol 119 (5) ◽  
pp. 1863-1878 ◽  
Author(s):  
Vahid Rahmati ◽  
Knut Kirmse ◽  
Knut Holthoff ◽  
Stefan J. Kiebel
Keyword(s):  
Calcium Imaging ◽  
Reconstruction Method ◽  
Imaging Data ◽  
Decay Kinetics ◽  
Experimental Conditions ◽  
Model Free ◽  
Reconstruction Methods ◽  
Wide Range ◽  
Fast Processing ◽  
Spiking Activity

Calcium imaging provides an indirect observation of the underlying neural dynamics and enables the functional analysis of neuronal populations. However, the recorded fluorescence traces are temporally smeared, thus making the reconstruction of exact spiking activity challenging. Most of the established methods to tackle this issue are limited in dealing with issues such as the variability in the kinetics of fluorescence transients, fast processing of long-term data, high firing rates, and measurement noise. We propose a novel, heuristic reconstruction method to overcome these limitations. By using both synthetic and experimental data, we demonstrate the four main features of this method: 1) it accurately reconstructs both isolated spikes and within-burst spikes, and the spike count per fluorescence transient, from a given noisy fluorescence trace; 2) it performs the reconstruction of a trace extracted from 1,000,000 frames in less than 2 s; 3) it adapts to transients with different rise and decay kinetics or amplitudes, both within and across single neurons; and 4) it has only one key parameter, which we will show can be set in a nearly automatic way to an approximately optimal value. Furthermore, we demonstrate the ability of the method to effectively correct for fast and rather complex, slowly varying drifts as frequently observed in in vivo data. NEW & NOTEWORTHY Reconstruction of spiking activities from calcium imaging data remains challenging. Most of the established reconstruction methods not only have limitations in adapting to systematic variations in the data and fast processing of large amounts of data, but their results also depend on the user’s experience. To overcome these limitations, we present a novel, heuristic model-free-type method that enables an ultra-fast, accurate, near-automatic reconstruction from data recorded under a wide range of experimental conditions.

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