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EXACT SPIKE TRAIN INFERENCE VIA ℓ0 OPTIMIZATION
- Source :
- Ann. Appl. Stat. 12, no. 4 (2018), 2457-2482
- Publication Year :
- 2018
-
Abstract
- 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 $\ell_1$ penalty was proposed for this task. In this paper, we slightly modify that recent proposal by replacing the $\ell_1$ penalty with an $\ell_0$ penalty. In stark contrast to the conventional wisdom that $\ell_0$ 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 $\ell_1$ proposal, in simulations as well as on two calcium imaging data sets. 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.<br />Comment: 28 pages, 6 figures
- Subjects :
- 0301 basic medicine
Statistics and Probability
FOS: Computer and information sciences
Optimization problem
Computer science
Open problem
Spike train
Statistics - Applications
Article
03 medical and health sciences
0302 clinical medicine
Software
Applications (stat.AP)
computer.programming_language
Discrete mathematics
dynamic programming
Computational neuroscience
Quantitative Biology::Neurons and Cognition
business.industry
fungi
Python (programming language)
Dynamic programming
calcium imaging
030104 developmental biology
nervous system
Modeling and Simulation
Quantitative Biology - Neurons and Cognition
FOS: Biological sciences
Convex optimization
Neurons and Cognition (q-bio.NC)
Statistics, Probability and Uncertainty
business
computer
changepoint detection
030217 neurology & neurosurgery
Neuroscience
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Ann. Appl. Stat. 12, no. 4 (2018), 2457-2482
- Accession number :
- edsair.doi.dedup.....3de79ea08c31d09a82db13beb667e9f1