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Bayesian target optimisation for high-precision holographic optogenetics
Two-photon optogenetics has transformed our ability to probe the structure and function of neural circuits. However, achieving precise optogenetic control of neural ensemble activity has remained fundamentally constrained by the problem of off-target stimulation (OTS): the inadvertent activation of...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10246014/ https://www.ncbi.nlm.nih.gov/pubmed/37292661 http://dx.doi.org/10.1101/2023.05.25.542307 |
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author | Triplett, Marcus A. Gajowa, Marta Adesnik, Hillel Paninski, Liam |
author_facet | Triplett, Marcus A. Gajowa, Marta Adesnik, Hillel Paninski, Liam |
author_sort | Triplett, Marcus A. |
collection | PubMed |
description | Two-photon optogenetics has transformed our ability to probe the structure and function of neural circuits. However, achieving precise optogenetic control of neural ensemble activity has remained fundamentally constrained by the problem of off-target stimulation (OTS): the inadvertent activation of nearby non-target neurons due to imperfect confinement of light onto target neurons. Here we propose a novel computational approach to this problem called Bayesian target optimisation. Our approach uses nonparametric Bayesian inference to model neural responses to optogenetic stimulation, and then optimises the laser powers and optical target locations needed to achieve a desired activity pattern with minimal OTS. We validate our approach in simulations and using data from in vitro experiments, showing that Bayesian target optimisation considerably reduces OTS across all conditions we test. Together, these results establish our ability to overcome OTS, enabling optogenetic stimulation with substantially improved precision. |
format | Online Article Text |
id | pubmed-10246014 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-102460142023-06-08 Bayesian target optimisation for high-precision holographic optogenetics Triplett, Marcus A. Gajowa, Marta Adesnik, Hillel Paninski, Liam bioRxiv Article Two-photon optogenetics has transformed our ability to probe the structure and function of neural circuits. However, achieving precise optogenetic control of neural ensemble activity has remained fundamentally constrained by the problem of off-target stimulation (OTS): the inadvertent activation of nearby non-target neurons due to imperfect confinement of light onto target neurons. Here we propose a novel computational approach to this problem called Bayesian target optimisation. Our approach uses nonparametric Bayesian inference to model neural responses to optogenetic stimulation, and then optimises the laser powers and optical target locations needed to achieve a desired activity pattern with minimal OTS. We validate our approach in simulations and using data from in vitro experiments, showing that Bayesian target optimisation considerably reduces OTS across all conditions we test. Together, these results establish our ability to overcome OTS, enabling optogenetic stimulation with substantially improved precision. Cold Spring Harbor Laboratory 2023-10-26 /pmc/articles/PMC10246014/ /pubmed/37292661 http://dx.doi.org/10.1101/2023.05.25.542307 Text en https://creativecommons.org/licenses/by-nd/4.0/This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License (https://creativecommons.org/licenses/by-nd/4.0/) , which allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Triplett, Marcus A. Gajowa, Marta Adesnik, Hillel Paninski, Liam Bayesian target optimisation for high-precision holographic optogenetics |
title | Bayesian target optimisation for high-precision holographic optogenetics |
title_full | Bayesian target optimisation for high-precision holographic optogenetics |
title_fullStr | Bayesian target optimisation for high-precision holographic optogenetics |
title_full_unstemmed | Bayesian target optimisation for high-precision holographic optogenetics |
title_short | Bayesian target optimisation for high-precision holographic optogenetics |
title_sort | bayesian target optimisation for high-precision holographic optogenetics |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10246014/ https://www.ncbi.nlm.nih.gov/pubmed/37292661 http://dx.doi.org/10.1101/2023.05.25.542307 |
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