ArticleeLife2020
Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics.
Article in eLife, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Cell-mechanical parameter estimation from 1D cell trajectories using simulation-based inference.PloS one · 2025Article
- Controlling morpho-electrophysiological variability of neurons with detailed biophysical models.iScience · 2023Article
- Simulation-based inference for efficient identification of generative models in computational connectomics.PLoS computational biology · 2023Article
- In vivo ephaptic coupling allows memory network formation.Cerebral cortex (New York, N.Y. : 1991) · 2023Article
- A theoretical model reveals specialized synaptic depressions and temporal frequency tuning in retinal parallel channels.Frontiers in computational neuroscience · 2022Article
- Mechanisms underlying activation of retinal bipolar cells through targeted electrical stimulation: a computational study.Journal of neural engineering · 2021Article
- Ancestral circuits for vertebrate color vision emerge at the first retinal synapse.Science advances · 2021Article
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- The impact of synchronous versus asynchronous electrical stimulation in artificial vision.Journal of neural engineering · 2021Review
- Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics.eLife · 2020Article
- Training deep neural density estimators to identify mechanistic models of neural dynamics.eLife · 2020Article
- Probing and predicting ganglion cell responses to smooth electrical stimulation in healthy and blind mouse retina.Scientific reports · 2020Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
While multicompartment models have long been used to study the biophysics of neurons, it is still challenging to infer the parameters of such models from data including uncertainty estimates. Here, we performed Bayesian inference for the parameters of detailed neuron models of a photoreceptor and an OFF- and an ON-cone bipolar cell from the mouse retina based on two-photon imaging data. We obtained multivariate posterior distributions specifying plausible parameter ranges consistent with the data and allowing to identify parameters poorly constrained by the data. To demonstrate the potential of such mechanistic data-driven neuron models, we created a simulation environment for external electrical stimulation of the retina and optimized stimulus waveforms to target OFF- and ON-cone bipolar cells, a current major problem of retinal neuroprosthetics.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.