Statistical Emulation of Neural Simulators: Application to Neocortical L2/3 Large Basket Cells
Many scientific systems are studied using computer codes that simulate the phenomena of interest. Computer simulation enables scientists to study a broad range of possible conditions, generating large quantities of data at a faster rate than the laboratory. Computer models are widespread in neurosci...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-03-01
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| Series: | Frontiers in Big Data |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fdata.2022.789962/full |
| _version_ | 1818315426531115008 |
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| author | Gilad Shapira Mira Marcus-Kalish Oren Amsalem Werner Van Geit Idan Segev David M. Steinberg |
| author_facet | Gilad Shapira Mira Marcus-Kalish Oren Amsalem Werner Van Geit Idan Segev David M. Steinberg |
| author_sort | Gilad Shapira |
| collection | DOAJ |
| description | Many scientific systems are studied using computer codes that simulate the phenomena of interest. Computer simulation enables scientists to study a broad range of possible conditions, generating large quantities of data at a faster rate than the laboratory. Computer models are widespread in neuroscience, where they are used to mimic brain function at different levels. These models offer a variety of new possibilities for the neuroscientist, but also numerous challenges, such as: where to sample the input space for the simulator, how to make sense of the data that is generated, and how to estimate unknown parameters in the model. Statistical emulation can be a valuable complement to simulator-based research. Emulators are able to mimic the simulator, often with a much smaller computational burden and they are especially valuable for parameter estimation, which may require many simulator evaluations. This work compares different statistical models that address these challenges, and applies them to simulations of neocortical L2/3 large basket cells, created and run with the NEURON simulator in the context of the European Human Brain Project. The novelty of our approach is the use of fast empirical emulators, which have the ability to accelerate the optimization process for the simulator and to identify which inputs (in this case, different membrane ion channels) are most influential in affecting simulated features. These contributions are complementary, as knowledge of the important features can further improve the optimization process. Subsequent research, conducted after the process is completed, will gain efficiency by focusing on these inputs. |
| first_indexed | 2024-12-13T09:05:21Z |
| format | Article |
| id | doaj.art-28b4bfa5788d4ecb875239ce24db0e6e |
| institution | Directory Open Access Journal |
| issn | 2624-909X |
| language | English |
| last_indexed | 2024-12-13T09:05:21Z |
| publishDate | 2022-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Big Data |
| spelling | doaj.art-28b4bfa5788d4ecb875239ce24db0e6e2022-12-21T23:53:05ZengFrontiers Media S.A.Frontiers in Big Data2624-909X2022-03-01510.3389/fdata.2022.789962789962Statistical Emulation of Neural Simulators: Application to Neocortical L2/3 Large Basket CellsGilad Shapira0Mira Marcus-Kalish1Oren Amsalem2Werner Van Geit3Idan Segev4David M. Steinberg5Department of Statistics and Operations Research, Tel Aviv University, Tel Aviv, IsraelDepartment of Statistics and Operations Research, Tel Aviv University, Tel Aviv, IsraelDivision of Endocrinology, Beth Israel Deaconess Medical Center, Harvard Medical School, Harvard University, Boston, MA, United StatesBlue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Campus Biotech, Geneva, SwitzerlandThe Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, IsraelDepartment of Statistics and Operations Research, Tel Aviv University, Tel Aviv, IsraelMany scientific systems are studied using computer codes that simulate the phenomena of interest. Computer simulation enables scientists to study a broad range of possible conditions, generating large quantities of data at a faster rate than the laboratory. Computer models are widespread in neuroscience, where they are used to mimic brain function at different levels. These models offer a variety of new possibilities for the neuroscientist, but also numerous challenges, such as: where to sample the input space for the simulator, how to make sense of the data that is generated, and how to estimate unknown parameters in the model. Statistical emulation can be a valuable complement to simulator-based research. Emulators are able to mimic the simulator, often with a much smaller computational burden and they are especially valuable for parameter estimation, which may require many simulator evaluations. This work compares different statistical models that address these challenges, and applies them to simulations of neocortical L2/3 large basket cells, created and run with the NEURON simulator in the context of the European Human Brain Project. The novelty of our approach is the use of fast empirical emulators, which have the ability to accelerate the optimization process for the simulator and to identify which inputs (in this case, different membrane ion channels) are most influential in affecting simulated features. These contributions are complementary, as knowledge of the important features can further improve the optimization process. Subsequent research, conducted after the process is completed, will gain efficiency by focusing on these inputs.https://www.frontiersin.org/articles/10.3389/fdata.2022.789962/fullemulatorGaussian processrandom forestin silico experimentneural networkNEURON simulator |
| spellingShingle | Gilad Shapira Mira Marcus-Kalish Oren Amsalem Werner Van Geit Idan Segev David M. Steinberg Statistical Emulation of Neural Simulators: Application to Neocortical L2/3 Large Basket Cells Frontiers in Big Data emulator Gaussian process random forest in silico experiment neural network NEURON simulator |
| title | Statistical Emulation of Neural Simulators: Application to Neocortical L2/3 Large Basket Cells |
| title_full | Statistical Emulation of Neural Simulators: Application to Neocortical L2/3 Large Basket Cells |
| title_fullStr | Statistical Emulation of Neural Simulators: Application to Neocortical L2/3 Large Basket Cells |
| title_full_unstemmed | Statistical Emulation of Neural Simulators: Application to Neocortical L2/3 Large Basket Cells |
| title_short | Statistical Emulation of Neural Simulators: Application to Neocortical L2/3 Large Basket Cells |
| title_sort | statistical emulation of neural simulators application to neocortical l2 3 large basket cells |
| topic | emulator Gaussian process random forest in silico experiment neural network NEURON simulator |
| url | https://www.frontiersin.org/articles/10.3389/fdata.2022.789962/full |
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