Statistical Emulation of Neural Simulators: Application to Neocortical L2/3 Large Basket Cells

Gilad Shapira, Mira Marcus-Kalish, Oren Amsalem, Werner Van Geit, Idan Segev, David M. Steinberg*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number789962
JournalFrontiers in Big Data
Volume5
DOIs
StatePublished - 25 Mar 2022

Funding

FundersFunder number
European Union's Horizon 2020 Framework Programme785907, 945539
Swiss Federal Institutes of Technology
Davis Family Foundation
Gatsby Charitable Foundation
École Polytechnique Fédérale de Lausanne
Tel Aviv University

    Keywords

    • Gaussian process
    • NEURON simulator
    • emulator
    • in silico experiment
    • neural network
    • random forest

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