TY - JOUR
T1 - Statistical Emulation of Neural Simulators
T2 - Application to Neocortical L2/3 Large Basket Cells
AU - Shapira, Gilad
AU - Marcus-Kalish, Mira
AU - Amsalem, Oren
AU - Van Geit, Werner
AU - Segev, Idan
AU - Steinberg, David M.
N1 - Publisher Copyright:
Copyright © 2022 Shapira, Marcus-Kalish, Amsalem, Van Geit, Segev and Steinberg.
PY - 2022/3/25
Y1 - 2022/3/25
N2 - 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.
AB - 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.
KW - Gaussian process
KW - NEURON simulator
KW - emulator
KW - in silico experiment
KW - neural network
KW - random forest
UR - http://www.scopus.com/inward/record.url?scp=85128337733&partnerID=8YFLogxK
U2 - 10.3389/fdata.2022.789962
DO - 10.3389/fdata.2022.789962
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C2 - 35402905
AN - SCOPUS:85128337733
SN - 2624-909X
VL - 5
JO - Frontiers in Big Data
JF - Frontiers in Big Data
M1 - 789962
ER -