Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics.
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ABSTRACT:
SUBMITTER: Oesterle J
PROVIDER: S-EPMC7673784 | biostudies-literature | 2020 Oct
REPOSITORIES: biostudies-literature
Publications
Bayesian inference for biophysical neuron models enables stimulus optimization for retinal neuroprosthetics.
Oesterle Jonathan J Behrens Christian C Schröder Cornelius C Hermann Thoralf T Euler Thomas T Franke Katrin K Smith Robert G RG Zeck Günther G Berens Philipp P
eLife 20201027
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 dat ...[more]
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