Estimating the biophysical properties of neurons with intracellular calcium dynamics

Ye, Jingxin; Rozdeba, Paul J.; Morone, Uriel; Daou, Arij

doi:10.1103/physreve.89.062714

Cited by 12 publications

(7 citation statements)

References 56 publications

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“…This specific formulation has been tested with chaotic models ( Abarbanel et al, 2011 ; Rey et al, 2014 ; Ye et al, 2014 , 2015 ), and used to estimate parameters in models of biological neurons ( Armstrong, 2020 ; Daniel Meliza et al, 2014 ; Kadakia et al, 2016 ; Kostuk et al, 2012 ; Toth et al, 2011 ; Wang et al, 2016 , pp. 584–587), as well as astrophysical scenarios ( ArmstrongAmol et al, 2017 ).…”

Section: Methodsmentioning

confidence: 99%

Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation

Armstrong¹,

Runge²,

Gerardin³

2021

Infectious Disease Modelling

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We demonstrate the ability of statistical data assimilation (SDA) to identify the measurements required for accurate state and parameter estimation in an epidemiological model for the novel coronavirus disease COVID-19. Our context is an effort to inform policy regarding social behavior, to mitigate strain on hospital capacity. The model unknowns are taken to be: the time-varying transmission rate, the fraction of exposed cases that require hospitalization, and the time-varying detection probabilities of new asymptomatic and symptomatic cases. In simulations, we obtain estimates of undetected (that is, unmeasured) infectious populations, by measuring the detected cases together with the recovered and dead - and without assumed knowledge of the detection rates. Given a noiseless measurement of the recovered population, excellent estimates of all quantities are obtained using a temporal baseline of 101 days, with the exception of the time-varying transmission rate at times prior to the implementation of social distancing. With low noise added to the recovered population, accurate state estimates require a lengthening of the temporal baseline of measurements. Estimates of all parameters are sensitive to the contamination, highlighting the need for accurate and uniform methods of reporting. The aim of this paper is to exemplify the power of SDA to determine what properties of measurements will yield estimates of unknown parameters to a desired precision, in a model with the complexity required to capture important features of the COVID-19 pandemic.

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Section: Methodsmentioning

confidence: 99%

Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation

Armstrong¹,

Runge²,

Gerardin³

2021

Infectious Disease Modelling

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show abstract

“…The data assimilation procedure is an estimation and validation method for unknown parameters and unobserved state variables in a physical model. It formulates the statistical problem as a high-dimensional path integral and has been explored both in its exact and approximate form on chaotic and neural models [6,7,8,9,10,11,12,13]. Here, we show that using simulated data one can accurately estimate the parameters and forward predict all states, measured and unmeasured, in an HVC RA neuron model using this procedure.…”

Section: Introductionmentioning

confidence: 93%

Nonlinear statistical data assimilation for HVC $$_{\mathrm{RA}}$$ RA neurons in the avian song system

Kadakia

Armstrong²,

Breen

et al. 2016

Biol Cybern

Self Cite

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With the goal of building a model of the HVC nucleus in the avian song system, we discuss in detail a model of HVC[Formula: see text] projection neurons comprised of a somatic compartment with fast Na[Formula: see text] and K[Formula: see text] currents and a dendritic compartment with slower Ca[Formula: see text] dynamics. We show this model qualitatively exhibits many observed electrophysiological behaviors. We then show in numerical procedures how one can design and analyze feasible laboratory experiments that allow the estimation of all of the many parameters and unmeasured dynamical variables, given observations of the somatic voltage [Formula: see text] alone. A key to this procedure is to initially estimate the slow dynamics associated with Ca, blocking the fast Na and K variations, and then with the Ca parameters fixed estimate the fast Na and K dynamics. This separation of time scales provides a numerically robust method for completing the full neuron model, and the efficacy of the method is tested by prediction when observations are complete. The simulation provides a framework for the slice preparation experiments and illustrates the use of data assimilation methods for the design of those experiments.

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“…This specific formulation has been tested with chaotic models [27][28][29][30], and used to estimate parameters in models of biological neurons [12,13,15,17,31,32], as well as astrophysical scenarios [33].…”

Section: B Optimization Frameworkmentioning

confidence: 99%

Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation

Armstrong¹,

Runge²,

Gerardin³

2020

Preprint

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We demonstrate the ability of statistical data assimilation to identify the measurements required for accurate state and parameter estimation in an epidemiological model for the novel coronavirus disease COVID-19. Our context is an effort to inform policy regarding social behavior, to mitigate strain on hospital capacity. The model unknowns are taken to be: the time-varying transmission rate, the fraction of exposed cases that require hospitalization, and the time-varying detection probabilities of new asymptomatic and symptomatic cases. In simulations, we obtain accurate estimates of undetected (that is, unmeasured) infectious populations, by measuring the detected cases together with the recovered and dead -and without assumed knowledge of the detection rates. These state estimates require a measurement of the recovered population, and are tolerant to low errors in that measurement. Further, excellent estimates of all quantities are obtained using a temporal baseline of 112 days, with the exception of the time-varying transmission rate at times prior to the implementation of social distancing. The estimation of this transmission rate is sensitive to contamination in the data, highlighting the need for accurate and uniform methods of reporting. Finally, we employ the procedure using real data from Italy reported by Johns Hopkins. The aim of this paper is not to assign extreme significance to the results of these specific experiments per se. Rather, we intend to exemplify the power of SDA to determine what properties of measurements will yield estimates of unknown model parameters to a desired precision -all set within the complex context of the COVID-19 pandemic.

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Estimating the biophysical properties of neurons with intracellular calcium dynamics

Cited by 12 publications

References 56 publications

Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation

Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation

Nonlinear statistical data assimilation for HVC $$_{\mathrm{RA}}$$ RA neurons in the avian song system

Identifying the measurements required to estimate rates of COVID-19 transmission, infection, and detection, using variational data assimilation

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