Correction: Corrigendum: Sparse deconvolution of high-density super-resolution images

Hugelier, Siewert; Rooi, Johan J. de; Bernex, Romain; Duwé, Sam; Devos, O.; Śliwa, M.; Dedecker, Peter; Eilers, Paul H.C.; Ruckebusch, Cyril

doi:10.1038/srep24975

Sci Rep

2016

DOI: 10.1038/srep24975

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Correction: Corrigendum: Sparse deconvolution of high-density super-resolution images

Siewert Hugelier¹,

Johan J. de Rooi²,

Romain Bernex³

et al.

Abstract: Scientific Reports 6: Article number: 21413; Published online 25 February 2016; Updated: 09 May 2016 A marked-up version of the Supplementary Information was inadvertently published with the original version of this Article. In addition, Supplementary Software 1 was omitted. These errors have now been corrected.

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Exact spike train inference via $\ell_{0}$ optimization

Jewell¹,

Witten²

2018

Ann. Appl. Stat.

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In recent years new technologies in neuroscience have made it possible to measure the activities of large numbers of neurons simultaneously in behaving animals. For each neuron a fluorescence trace is measured; this can be seen as a first-order approximation of the neuron’s activity over time. Determining the exact time at which a neuron spikes on the basis of its fluorescence trace is an important open problem in the field of computational neuroscience. Recently, a convex optimization problem involving an ℓ1 penalty was proposed for this task. In this paper we slightly modify that recent proposal by replacing the ℓ1 penalty with an ℓ0 penalty. In stark contrast to the conventional wisdom that ℓ0 optimization problems are computationally intractable, we show that the resulting optimization problem can be efficiently solved for the global optimum using an extremely simple and efficient dynamic programming algorithm. Our R-language implementation of the proposed algorithm runs in a few minutes on fluorescence traces of 100,000 timesteps. Furthermore, our proposal leads to substantial improvements over the previous ℓ1 proposal, in simulations as well as on two calcium imaging datasets. R-language software for our proposal is available on CRAN in the package LZeroSpikeInference. Instructions for running this software in python can be found at https://github.com/jewellsean/LZeroSpikeInference.

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Exact spike train inference via $\ell_{0}$ optimization

Jewell¹,

Witten²

2018

Ann. Appl. Stat.

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Correction: Corrigendum: Sparse deconvolution of high-density super-resolution images

Cited by 1 publication

References 0 publications

Exact spike train inference via $\ell_{0}$ optimization

Exact spike train inference via $\ell_{0}$ optimization

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