@article {48716,
	title = {Multi-scale approaches for high-speed imaging and analysis of large neural populations.},
	journal = {PLoS Computational Biology},
	volume = {13},
	year = {2017},
	month = {2017 Aug 03},
	pages = {e1005685},
	abstract = {<p>Progress in modern neuroscience critically depends on our ability to observe the activity of large neuronal populations with cellular spatial and high temporal resolution. However, two bottlenecks constrain efforts towards fast imaging of large populations. First, the resulting large video data is challenging to analyze. Second, there is an explicit tradeoff between imaging speed, signal-to-noise, and field of view: with current recording technology we cannot image very large neuronal populations with simultaneously high spatial and temporal resolution. Here we describe multi-scale approaches for alleviating both of these bottlenecks. First, we show that spatial and temporal decimation techniques based on simple local averaging provide order-of-magnitude speedups in spatiotemporally demixing calcium video data into estimates of single-cell neural activity. Second, once the shapes of individual neurons have been identified at fine scale (e.g., after an initial phase of conventional imaging with standard temporal and spatial resolution), we find that the spatial/temporal resolution tradeoff shifts dramatically: after demixing we can accurately recover denoised fluorescence traces and deconvolved neural activity of each individual neuron from coarse scale data that has been spatially decimated by an order of magnitude. This offers a cheap method for compressing this large video data, and also implies that it is possible to either speed up imaging significantly, or to "zoom out" by a corresponding factor to image order-of-magnitude larger neuronal populations with minimal loss in accuracy or temporal resolution.</p>
},
	issn = {1553-7358},
	doi = {10.1371/journal.pcbi.1005685},
	author = {Friedrich, Johannes and Yang, Weijian and Soudry, Daniel and Mu, Yu and Ahrens, Misha B and Yuste, Rafael and Peterka, Darcy S and Paninski, Liam}
}
@article {47262,
	title = {Simultaneous denoising, deconvolution, and demixing of calcium imaging data.},
	journal = {Neuron},
	volume = {89},
	year = {2016},
	month = {2016 Jan 20},
	pages = {285-99},
	abstract = {<p>We present a modular approach for analyzing calcium imaging recordings of large neuronal ensembles. Our goal is to simultaneously identify the locations of the neurons, demix spatially overlapping components, and denoise and deconvolve the spiking activity from the slow dynamics of the calcium indicator. Our approach relies on a constrained nonnegative matrix factorization that expresses the spatiotemporal fluorescence activity as the product of a spatial matrix that encodes the spatial footprint of each neuron in the optical field and a temporal matrix that characterizes the calcium concentration of each neuron over time. This framework is combined with a novel constrained deconvolution approach that extracts estimates of neural activity from fluorescence traces, to create a spatiotemporal processing algorithm that requires minimal parameter tuning. We demonstrate the general applicability of our method by applying it to in\&nbsp;vitro and in\&nbsp;vivo multi-neuronal imaging data, whole-brain light-sheet imaging data, and dendritic imaging data.</p>
},
	issn = {1097-4199},
	doi = {10.1016/j.neuron.2015.11.037},
	author = {Pnevmatikakis, Eftychios A and Soudry, Daniel and Gao, Yuanjun and Machado, Timothy A and Merel, Josh and Pfau, David and Reardon, Thomas and Mu, Yu and Lacefield, Clay and Yang, Weijian and Ahrens, Misha and Bruno, Randy and Jessell, Thomas M and Peterka, Darcy S and Yuste, Rafael and Paninski, Liam}
}
