Spike Estimation From Fluorescence Signals Using High-Resolution Property of Group Delay

Sebastian, Jilt; Kumar, Mari Ganesh; Viraraghavan, Venkata Subramanian; Sur, Mriganka; Murthy, Hema A.

doi:10.1109/tsp.2019.2908913

Cited by 11 publications

(15 citation statements)

References 33 publications

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“…We can now use the expressions of f θ t and g θ t in Eqs. (11,12) for our model to compute the optimal proposal distribution. To do so we decompose P (X t |X t−1 , F t ) as the product…”

Section: Particle Gibbs With Ancestor Samplingmentioning

confidence: 99%

“…By combining the expressions in Eqs. (10), (11) and (12) with the gamma prior gamma(α, β) and by keeping only terms proportional to r 0,1 we obtain…”

Section: Sampling Rules For Static Parametersmentioning

confidence: 99%

“…Fluorescence indicators of calcium activity allow us to monitor the dynamics of neuronal populations both in vivo and in vitro. In the last decade there has been a proliferation of new methods to identify single spikes from fluorescence time series using template matching [1][2][3][4], linear deconvolution [5][6][7][8], finite rate of innovation [9,10], independent component analysis [11], non model-based signal processing [12], supervised learning [13][14][15][16][17], constrained non-negative matrix factorization [18][19][20], active set methods [21,22], convex and non-convex optimization methods [23][24][25][26][27], interior point method [28]. Model-based approaches allow to frame the problem of spike inference in a Bayesian context and use maximum-a-posteriori estimates [29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

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High frequency spike inference with particle Gibbs sampling

Diana

Sermet

DiGregorio

2022

Preprint

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Fluorescent calcium indicators are an indispensable tools for monitoring the spiking activity of large neuronal populations in animal models. However, despite the plethora of algorithms developed over the last decades, accurate spike time inference methods for rates greater than 20 Hz are lacking. More importantly, little attention has been devoted to the quantification the statistical uncertainties in spike time estimation, which is essential for assigning a confidence in the inference for a particular recording. To address these challenges, we introduce an auto-regressive generative model that accounts for bursting neuronal activity and baseline fluorescence modulation, and it also applies recent sequential Monte Carlo approaches to obtain joint posterior distributions of static and dynamic model parameters. We show that our inference method is competitive with state-of-the-art algorithms by analysing the CASCADE benchmark datasets. We also show that spike time intervals as short as five milliseconds can be inferred from fluorescence transients recorded using a state-of-the-art genetically encoded indicator. Overall, our study describes a Bayesian inference method to detect neuronal spiking patterns and their uncertainty. The use of particle Gibbs samplers allows for unbiased estimates of all model parameters and it provides a statistical framework to test more specific models of calcium indicators.

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Section: Particle Gibbs With Ancestor Samplingmentioning

confidence: 99%

“…By combining the expressions in Eqs. (10), (11) and (12) with the gamma prior gamma(α, β) and by keeping only terms proportional to r 0,1 we obtain…”

Section: Sampling Rules For Static Parametersmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

High frequency spike inference with particle Gibbs sampling

Diana

Sermet

DiGregorio

2022

Preprint

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show abstract

“…These models are limited by the apriori assumptions about the model, which has stringent approximations regarding the shape of the calcium response and the noise statistics. Recently, a non-model based signal processing approach for spike estimation is presented in [ 16 ] that uses the agnostic nature of group delay to estimate the spike locations. It has a comparable performance with other popular algorithms in the literature.…”

Section: Introductionmentioning

confidence: 99%

Signal-to-signal neural networks for improved spike estimation from calcium imaging data

et al. 2021

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Spiking information of individual neurons is essential for functional and behavioral analysis in neuroscience research. Calcium imaging techniques are generally employed to obtain activities of neuronal populations. However, these techniques result in slowly-varying fluorescence signals with low temporal resolution. Estimating the temporal positions of the neuronal action potentials from these signals is a challenging problem. In the literature, several generative model-based and data-driven algorithms have been studied with varied levels of success. This article proposes a neural network-based signal-to-signal conversion approach, where it takes as input raw-fluorescence signal and learns to estimate the spike information in an end-to-end fashion. Theoretically, the proposed approach formulates the spike estimation as a single channel source separation problem with unknown mixing conditions. The source corresponding to the action potentials at a lower resolution is estimated at the output. Experimental studies on the spikefinder challenge dataset show that the proposed signal-to-signal conversion approach significantly outperforms state-of-the-art-methods in terms of Pearson’s correlation coefficient, Spearman’s rank correlation coefficient and yields comparable performance for the area under the receiver operating characteristics measure. We also show that the resulting system: (a) has low complexity with respect to existing supervised approaches and is reproducible; (b) is layer-wise interpretable, and (c) has the capability to generalize across different calcium indicators.

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“…Some other works also use feature extraction and thresholding 40,41 (e.g. GDspike 41 ) to tackle the problem of spike inference.…”

Section: Introductionmentioning

confidence: 99%

Effective and Efficient Neural Networks for Spike Inference fromIn VivoCalcium Imaging

Zhou

Tin

2021

Preprint

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Calcium imaging technique provides irreplaceable advantages in monitoring large population of neuronal activities simultaneously. However, due to the generally low signal to noise ratio (SNR) of the calcium signal and variability in dye properties, it is still challenging to faithfully infer neuronal spikes from these calcium signals, especially from in vivo experiments. In this study, we tackled the problem of both spike-rate and spike-event predictions using a data-driven approach, based on a public pool of dataset with simultaneously recorded calcium and electrophysiological signals using different dyes and recorded from different brain regions. We proposed the ENS2 (effective and efficient neural networks for spike inference from calcium signals) system using raw calcium inputs and it consistently outperforms state-of-the-arts algorithms in both spike-rate and spike-event predictions with reduced computational load. We have also demonstrated that factors such as sampling rates, smoothing window sizes and parametric evaluation metrics could readily bias the interpretation of inference performance. We concluded that optimizing our system for spike-event prediction could produce a more versatile inference system for real neuroscience studies.

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Spike Estimation From Fluorescence Signals Using High-Resolution Property of Group Delay

Cited by 11 publications

References 33 publications

High frequency spike inference with particle Gibbs sampling

High frequency spike inference with particle Gibbs sampling

Signal-to-signal neural networks for improved spike estimation from calcium imaging data

Effective and Efficient Neural Networks for Spike Inference fromIn VivoCalcium Imaging

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