Evidence map›Paper›PMID 34220478›Full record

ArticleFrontiers in computational neuroscience2021

Double Two-State Opsin Model With Autonomous Parameter Inference.

Ruben Schoeters, Thomas Tarnaud, Luc Martens, Wout Joseph, Robrecht Raedt, Emmeric Tanghe

Abstract read
In one paragraph

Article in Frontiers in computational neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Quantitative analysis of the optogenetic excitability of CA1 neurons.Frontiers in computational neuroscience · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ruben SchoetersWAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium.
Thomas TarnaudWAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium.
Luc MartensWAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium.
Wout JosephWAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium.
Robrecht Raedt4BRAIN, Department of Neurology, Institute for Neuroscience, Ghent University, Ghent, Belgium.
Emmeric TangheWAVES, Department of Information Technology (INTEC), Ghent University/IMEC, Ghent, Belgium.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Optogenetics has a lot of potential to become an effective neuromodulative therapy for clinical applications. Selecting the correct opsin is crucial to have an optimal optogenetic tool. With computational modeling, the neuronal response to the current dynamics of an opsin can be extensively and systematically tested. Unlike electrical stimulation where the effect is directly defined by the applied field, the stimulation in optogenetics is indirect, depending on the selected opsin's non-linear kinetics. With the continuous expansion of opsin possibilities, computational studies are difficult due to the need for an accurate model of the selected opsin first. To this end, we propose a double two-state opsin model as alternative to the conventional three and four state Markov models used for opsin modeling. Furthermore, we provide a fitting procedure, which allows for autonomous model fitting starting from a vast parameter space. With this procedure, we successfully fitted two distinctive opsins (ChR2(H134R) and MerMAID). Both models are able to represent the experimental data with great accuracy and were obtained within an acceptable time frame. This is due to the absence of differential equations in the fitting procedure, with an enormous reduction in computational cost as result. The performance of the proposed model with a fit to ChR2(H134R) was tested, by comparing the neural response in a regular spiking neuron to the response obtained with the non-instantaneous, four state Markov model (4SB), derived by Williams et al. (2013). Finally, a computational speed gain was observed with the proposed model in a regular spiking and sparse Pyramidal-Interneuron-Network-Gamma (sPING) network simulation with respect to the 4SB-model, due to the former having two differential equations less. Consequently, the proposed model allows for computationally efficient optogenetic neurostimulation and with the proposed fitting procedure will be valuable for further research in the field of optogenetics.

Indexed as

channelrhodopsin-H134Rcomputational efficiencycomputational optogeneticsMerMAIDmodel fitting

Identifiers

PMID34220478
PMCPMC8243001

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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.