Cargando…

Double Two-State Opsin Model With Autonomous Parameter Inference

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 syst...

Descripción completa

Detalles Bibliográficos
Autores principales: Schoeters, Ruben, Tarnaud, Thomas, Martens, Luc, Joseph, Wout, Raedt, Robrecht, Tanghe, Emmeric
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8243001/
https://www.ncbi.nlm.nih.gov/pubmed/34220478
http://dx.doi.org/10.3389/fncom.2021.688331
_version_ 1783715679677972480
author Schoeters, Ruben
Tarnaud, Thomas
Martens, Luc
Joseph, Wout
Raedt, Robrecht
Tanghe, Emmeric
author_facet Schoeters, Ruben
Tarnaud, Thomas
Martens, Luc
Joseph, Wout
Raedt, Robrecht
Tanghe, Emmeric
author_sort Schoeters, Ruben
collection PubMed
description 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.
format Online
Article
Text
id pubmed-8243001
institution National Center for Biotechnology Information
language English
publishDate 2021
publisher Frontiers Media S.A.
record_format MEDLINE/PubMed
spelling pubmed-82430012021-07-01 Double Two-State Opsin Model With Autonomous Parameter Inference Schoeters, Ruben Tarnaud, Thomas Martens, Luc Joseph, Wout Raedt, Robrecht Tanghe, Emmeric Front Comput Neurosci Neuroscience 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. Frontiers Media S.A. 2021-06-16 /pmc/articles/PMC8243001/ /pubmed/34220478 http://dx.doi.org/10.3389/fncom.2021.688331 Text en Copyright © 2021 Schoeters, Tarnaud, Martens, Joseph, Raedt and Tanghe. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Schoeters, Ruben
Tarnaud, Thomas
Martens, Luc
Joseph, Wout
Raedt, Robrecht
Tanghe, Emmeric
Double Two-State Opsin Model With Autonomous Parameter Inference
title Double Two-State Opsin Model With Autonomous Parameter Inference
title_full Double Two-State Opsin Model With Autonomous Parameter Inference
title_fullStr Double Two-State Opsin Model With Autonomous Parameter Inference
title_full_unstemmed Double Two-State Opsin Model With Autonomous Parameter Inference
title_short Double Two-State Opsin Model With Autonomous Parameter Inference
title_sort double two-state opsin model with autonomous parameter inference
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8243001/
https://www.ncbi.nlm.nih.gov/pubmed/34220478
http://dx.doi.org/10.3389/fncom.2021.688331
work_keys_str_mv AT schoetersruben doubletwostateopsinmodelwithautonomousparameterinference
AT tarnaudthomas doubletwostateopsinmodelwithautonomousparameterinference
AT martensluc doubletwostateopsinmodelwithautonomousparameterinference
AT josephwout doubletwostateopsinmodelwithautonomousparameterinference
AT raedtrobrecht doubletwostateopsinmodelwithautonomousparameterinference
AT tangheemmeric doubletwostateopsinmodelwithautonomousparameterinference