Review of data processing of functional optical microscopy for neuroscience

Benisty, H.; Song, Alexander; Mishne, Gal; Charles, Adam S.

doi:10.1117/1.nph.9.4.041402

Cited by 6 publications

(5 citation statements)

References 217 publications

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“…We chose in this study to primarily analyze the normalized fluorescence traces (Δ𝐹 /𝐹) rather than using deconvolution or spike inference methods (see [99][100][101] for a review). Deconvolution methods were developed in part due to the slow temporal dynamics of the calcium indicators relative to membrane potentials generating spiking activity [102,103]. Deconvolution and other spike inference techniques attempt to mitigate this limitation for analyses that depend on more exact measures of spike timing, and developers note these methods should be avoided when temporal information is not relevant and the raw calcium traces provide "sufficient information" [100].…”

Section: Discussionmentioning

confidence: 99%

Modeling the Diverse Effects of Divisive Normalization on Noise Correlations

Weiss

Bounds

Adesnik

et al. 2022

Preprint

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Divisive normalization, a prominent model of interactions between neurons, plays a key role in influential theories of neural coding. Testing the mechanistic basis of those theories requires understanding the relationship between normalization and the statistics of neural activity, particularly noise correlations which provide strong constrains on circuit models. Yet the relation between normalization and correlations remains unclear, because models of covariability typically ignore normalization despite empirical evidence suggesting it contributes to stimulus and state modulations of covariability. We propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability, and accurately fits Ca2+ imaging data from mouse primary visual cortex (V1). We show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we outline the data regime in which the corresponding model parameter is identifiable. Our analysis provides the first quantitative evidence that similarly tuned neurons in mouse V1 share their normalization signals, and explains our observation that stronger normalization decorrelates those pairs, particularly at low stimulus contrast. Our model could enable quantifying the role of normalization on information transmission and representation, and mapping the parameters of normalization onto circuit and cellular mechanisms.

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Section: Discussionmentioning

confidence: 99%

Modeling the Diverse Effects of Divisive Normalization on Noise Correlations

Weiss

Bounds

Adesnik

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…We chose in this study to primarily analyze the normalized fluorescence traces (ΔF/F) rather than using deconvolution or spike inference methods (see [103][104][105] for a review). Deconvolution methods were developed in part due to the slow temporal dynamics of the calcium indicators relative to membrane potentials generating spiking activity [106,107]. Deconvolution and other spike inference techniques attempt to mitigate this limitation for analyses that depend on more exact measures of spike timing, and developers note these methods should be avoided when temporal information is not relevant and the raw calcium traces provide "sufficient information" [104].…”

Section: Plos Computational Biologymentioning

confidence: 99%

Modeling the diverse effects of divisive normalization on noise correlations

Weiss,

Bounds,

Adesnik

et al. 2023

PLoS Comput Biol

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Divisive normalization, a prominent descriptive model of neural activity, is employed by theories of neural coding across many different brain areas. Yet, the relationship between normalization and the statistics of neural responses beyond single neurons remains largely unexplored. Here we focus on noise correlations, a widely studied pairwise statistic, because its stimulus and state dependence plays a central role in neural coding. Existing models of covariability typically ignore normalization despite empirical evidence suggesting it affects correlation structure in neural populations. We therefore propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability. We first show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we discuss how to infer when normalization signals are shared. We then apply our model to calcium imaging data from mouse primary visual cortex (V1), and find that it accurately fits the data, often outperforming a popular alternative model of correlations. Our analysis indicates that normalization signals are often shared between V1 neurons in this dataset. Our model will enable quantifying the relation between normalization and covariability in a broad range of neural systems, which could provide new constraints on circuit mechanisms of normalization and their role in information transmission and representation.

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“…Data science, in the form of statistical modeling and image processing, has become a central topic in extracting the most out of complex imaging data, such as taken by the latest in optical designs. 1 Computational approaches in particular have blossomed as imaging methods have extended across the micro-and macro-scales that aim to glean information about how the brain processes information at all scales. 2 These methods fundamentally take into account-implicitly or explicitly-properties of the data imbued by the imaging target and the imaging device.…”

Section: Introductionmentioning

confidence: 99%

Deep and shallow data science for multi-scale optical neuroscience

Mishne,

Charles

2024

Neural Imaging and Sensing 2024

Self Cite

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Optical imaging of the brain has expanded dramatically in the past two decades. New optics, indicators, and experimental paradigms are now enabling in-vivo imaging from the synaptic to the cortex-wide scales. To match the resulting flood of data across scales, computational methods are continuously being developed to meet the need of extracting biologically relevant information. In this pursuit challenges arise in some domains (e.g., SNR and resolution limits in micron-scale data) that require specialized algorithms. These algorithms can, for example, make use of state-of-the-art machine learning to maximally learn the details of a given scale to optimize the processing pipeline. In contrast, other methods, however, such as graph signal processing, seek to abstract away from some of the details that are scale-specific to provide solution to specific sub-problems common across scales of neuroimaging. Here we discuss limitations and tradeoffs in algorithmic design with the goal of identifying how data quality and variability can hamper algorithm use and dissemination.

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Review of data processing of functional optical microscopy for neuroscience

Cited by 6 publications

References 217 publications

Modeling the Diverse Effects of Divisive Normalization on Noise Correlations

Modeling the Diverse Effects of Divisive Normalization on Noise Correlations

Modeling the diverse effects of divisive normalization on noise correlations

Deep and shallow data science for multi-scale optical neuroscience

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