Counting with Analog Neurons

Šíma, Jiří

doi:10.1007/978-3-030-30487-4_31

Cited by 2 publications

(1 citation statement)

References 19 publications

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“…Further, dynamical non-linear methods have been applied to recover neural trajectories as a function of latent variables 26 . Linear DR methods have likewise been employed to either statically cluster cells 18,27–29 , or to abstractly analyze activity in low-dimensional subspaces 30–33 .…”

Section: Introductionmentioning

confidence: 99%

Non-Negative Matrix Factorization for Analyzing State Dependent Neuronal Network Dynamics in Calcium Recordings

Carbonero,

Noueihed,

Kramer

et al. 2023

Preprint

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Calcium imaging allows recording from hundreds of neurons in vivo with the ability to resolve single cell activity. Evaluating and analyzing neuronal responses, while also considering all dimensions of the data set to make specific conclusions, is extremely difficult. Often, descriptive statistics are used to analyze these forms of data. These analyses, however, remove variance by averaging the responses of single neurons across recording sessions, or across combinations of neurons to create single quantitative metrics, losing the temporal dynamics of neuronal activity, and their responses relative to each other. Dimensionally Reduction (DR) methods serve as a good foundation for these analyses because they reduce the dimensions of the data into components, while still maintaining the variance. Non-negative Matrix Factorization (NMF) is an especially promising DR analysis method for calcium imaging because of its mathematical constraints, which include positivity and linearity. We adapt NMF for our analyses and compare its performance to alternative dimensionality reduction methods on both artificial and in vivo data. We find that NMF is well-suited for analyzing calcium imaging recordings, accurately capturing the underlying dynamics of the data, and outperforming alternative methods in common use.

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