Simulation-Based Inference for Global Health Decisions

Witt, Christian Schroeder de; Gram-Hansen, Bradley; Nardelli, Nantas; Gambardella, Andrew; Zinkov, Rob; Dokania, Puneet K.; Siddharth, N.; Espinosa-González, Ana Belén; Darzi, Ara; Han, Junwei; Baydin, Atılım Güneş

doi:10.48550/arxiv.2005.07062

Cited by 2 publications

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“…To account for this limitation, we employed simulation-based inference (SBI, [25]) methods which only require simulations from the model to perform Bayesian inference. SBI has been applied previously in various fields, ranging from genomics [50], evolutionary biology [51,52], computational and cognitive neuroscience [26,[53][54][55][56][57], to robotics [58], global health [59] and astrophysics [60,61]. For the wiring rule examples presented here, we used sequential neural posterior estimation (SNPE, [27][28][29]), which performs neural-networkbased conditional density estimation to estimate the posterior distribution from simulated data.…”

Section: Related Workmentioning

confidence: 99%

Simulation-based inference for efficient identification of generative models in computational connectomics

Boelts,

Harth,

Gao

et al. 2023

PLoS Comput Biol

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Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neuronal networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters, and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a fixed wiring rule to fit the empirical data, SBI considers many parametrizations of a rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rule parameters and relies on machine learning methods to estimate a probability distribution (the ‘posterior distribution over parameters conditioned on the data’) that characterizes all data-compatible parameters. We demonstrate how to apply SBI in computational connectomics by inferring the parameters of wiring rules in an in silico model of the rat barrel cortex, given in vivo connectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data.

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Section: Related Workmentioning

confidence: 99%

Simulation-based inference for efficient identification of generative models in computational connectomics

Boelts,

Harth,

Gao

et al. 2023

PLoS Comput Biol

View full text Add to dashboard Cite

show abstract

“…To account for this limitation, we employed simulation-based inference (SBI, Cranmer et al, 2020 ) methods which only require simulations from the model to perform Bayesian inference. SBI has been applied previously in various fields, ranging from genomics ( Bernstein et al, 2021 ), evolutionary biology ( Ratmann et al, 2007 ; Avecilla et al, 2022 ), computational and cognitive neuroscience ( Gonçalves et al, 2020 ; Oesterle et al, 2020 ; Deistler et al, 2022b ; Groschner et al, 2022 ; Sabbagh et al, 2020 ; Hashemi et al, 2022 ), to robotics ( Marlier et al, 2021 ), global health ( de Witt et al, 2020 ) and astrophysics ( Alsing et al, 2018 ; Dax et al, 2021 ). For the wiring rule examples presented here, we used sequential neural posterior estimation (SNPE, Papamakarios and Murray, 2016 ; Lueckmann et al, 2017 ; Greenberg et al, 2019 ), which performs neural-network-based conditional density estimation to estimate the posterior distribution from simulated data.…”

Section: Discussionmentioning

confidence: 99%

Simulation-based inference for efficient identification of generative models in computational connectomics

Boelts

Harth

Gao

et al. 2023

Preprint

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Recent advances in connectomics research enable the acquisition of increasing amounts of data about the connectivity patterns of neurons. How can we use this wealth of data to efficiently derive and test hypotheses about the principles underlying these patterns? A common approach is to simulate neural networks using a hypothesized wiring rule in a generative model and to compare the resulting synthetic data with empirical data. However, most wiring rules have at least some free parameters and identifying parameters that reproduce empirical data can be challenging as it often requires manual parameter tuning. Here, we propose to use simulation-based Bayesian inference (SBI) to address this challenge. Rather than optimizing a single rule to fit the empirical data, SBI considers many parametrizations of a wiring rule and performs Bayesian inference to identify the parameters that are compatible with the data. It uses simulated data from multiple candidate wiring rules and relies on machine learning methods to estimate a probability distribution (the `posterior distribution over rule parameters conditioned on the data') that characterizes all data-compatible rules. We demonstrate how to apply SBI in connectomics by inferring the parameters of wiring rules in anin silicomodel of the rat barrel cortex, givenin vivoconnectivity measurements. SBI identifies a wide range of wiring rule parameters that reproduce the measurements. We show how access to the posterior distribution over all data-compatible parameters allows us to analyze their relationship, revealing biologically plausible parameter interactions and enabling experimentally testable predictions. We further show how SBI can be applied to wiring rules at different spatial scales to quantitatively rule out invalid wiring hypotheses. Our approach is applicable to a wide range of generative models used in connectomics, providing a quantitative and efficient way to constrain model parameters with empirical connectivity data.

show abstract

Simulation-Based Inference for Global Health Decisions

Cited by 2 publications

References 0 publications

Simulation-based inference for efficient identification of generative models in computational connectomics

Simulation-based inference for efficient identification of generative models in computational connectomics

Simulation-based inference for efficient identification of generative models in computational connectomics

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