Double Two-State Opsin Model With Autonomous Parameter Inference.
MerMAID
channelrhodopsin-H134R
computational efficiency
computational optogenetics
model fitting
Journal
Frontiers in computational neuroscience
ISSN: 1662-5188
Titre abrégé: Front Comput Neurosci
Pays: Switzerland
ID NLM: 101477956
Informations de publication
Date de publication:
2021
2021
Historique:
received:
30
03
2021
accepted:
21
05
2021
entrez:
5
7
2021
pubmed:
6
7
2021
medline:
6
7
2021
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 34220478
doi: 10.3389/fncom.2021.688331
pmc: PMC8243001
doi:
Types de publication
Journal Article
Langues
eng
Pagination
688331Informations de copyright
Copyright © 2021 Schoeters, Tarnaud, Martens, Joseph, Raedt and Tanghe.
Déclaration de conflit d'intérêts
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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