Simulating Large-scale Models of Brain Neuronal Circuits using Google Cloud Platform

Subhashini Sivagnanam; Wyatt Gorman; Donald Doherty; Samuel A. Neymotin; Stephen Fang; Hermine Hovhannisyan; William W. Lytton; Salvador Dura-Bernal

doi:10.1145/3311790.3399621

Simulating Large-scale Models of Brain Neuronal Circuits using Google Cloud Platform

Subhashini Sivagnanam
, Wyatt Gorman
, Donald Doherty
, Samuel A. Neymotin
, Stephen Fang
, Hermine Hovhannisyan
, William W. Lytton
, Salvador Dura-Bernal

Physiology / Pharmacology

SUNY Downstate Health Sciences University

State University of New York DMC
University of California at San Diego
Alphabet Inc.
New York State Office of Mental Health

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Scopus citations

Abstract

Biophysically detailed modeling provides an unmatched method to integrate data from many disparate experimental studies, and manipulate and explore with high precision the resulting brain circuit simulation. We developed a detailed model of the brain motor cortex circuits, simulating over 10,000 biophysically detailed neurons and 30 million synaptic connections. Optimization and evaluation of the cortical model parameters and responses was achieved via parameter exploration using grid search parameter sweeps and evolutionary algorithms. This involves running tens of thousands of simulations requiring significant computational resources. This paper describes our experience in setting up and using Google Compute Platform (GCP) with Slurm to run these large-scale simulations. We describe the best practices and solutions to the issues that arose during the process, and present preliminary results from running simulations on GCP.

Original language	English
Title of host publication	PEARC 2020 - Practice and Experience in Advanced Research Computing 2020
Subtitle of host publication	Catch the Wave
Publisher	Association for Computing Machinery
Pages	505-509
Number of pages	5
ISBN (Electronic)	9781450366892
DOIs	https://doi.org/10.1145/3311790.3399621
State	Published - Jul 26 2020
Event	2020 Conference on Practice and Experience in Advanced Research Computing: Catch the Wave, PEARC 2020 - Virtual, Online, United States Duration: Jul 27 2020 → Jul 31 2020

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	2020 Conference on Practice and Experience in Advanced Research Computing: Catch the Wave, PEARC 2020
Country/Territory	United States
City	Virtual, Online
Period	07/27/20 → 07/31/20

Keywords

Brain modeling
Computational neuroscience
Google Cloud Platform
Large-scale simulations

Access to Document

10.1145/3311790.3399621

Cite this

Sivagnanam, S., Gorman, W., Doherty, D., Neymotin, S. A., Fang, S., Hovhannisyan, H., Lytton, W. W., & Dura-Bernal, S. (2020). Simulating Large-scale Models of Brain Neuronal Circuits using Google Cloud Platform. In PEARC 2020 - Practice and Experience in Advanced Research Computing 2020: Catch the Wave (pp. 505-509). (ACM International Conference Proceeding Series). Association for Computing Machinery. https://doi.org/10.1145/3311790.3399621

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title = "Simulating Large-scale Models of Brain Neuronal Circuits using Google Cloud Platform",

abstract = "Biophysically detailed modeling provides an unmatched method to integrate data from many disparate experimental studies, and manipulate and explore with high precision the resulting brain circuit simulation. We developed a detailed model of the brain motor cortex circuits, simulating over 10,000 biophysically detailed neurons and 30 million synaptic connections. Optimization and evaluation of the cortical model parameters and responses was achieved via parameter exploration using grid search parameter sweeps and evolutionary algorithms. This involves running tens of thousands of simulations requiring significant computational resources. This paper describes our experience in setting up and using Google Compute Platform (GCP) with Slurm to run these large-scale simulations. We describe the best practices and solutions to the issues that arose during the process, and present preliminary results from running simulations on GCP.",

keywords = "Brain modeling, Computational neuroscience, Google Cloud Platform, Large-scale simulations",

author = "Subhashini Sivagnanam and Wyatt Gorman and Donald Doherty and Neymotin, \{Samuel A.\} and Stephen Fang and Hermine Hovhannisyan and Lytton, \{William W.\} and Salvador Dura-Bernal",

note = "Publisher Copyright: {\textcopyright} 2020 Owner/Author.; 2020 Conference on Practice and Experience in Advanced Research Computing: Catch the Wave, PEARC 2020 ; Conference date: 27-07-2020 Through 31-07-2020",

year = "2020",

month = jul,

day = "26",

doi = "10.1145/3311790.3399621",

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Sivagnanam, S, Gorman, W, Doherty, D, Neymotin, SA, Fang, S, Hovhannisyan, H, Lytton, WW & Dura-Bernal, S 2020, Simulating Large-scale Models of Brain Neuronal Circuits using Google Cloud Platform. in PEARC 2020 - Practice and Experience in Advanced Research Computing 2020: Catch the Wave. ACM International Conference Proceeding Series, Association for Computing Machinery, pp. 505-509, 2020 Conference on Practice and Experience in Advanced Research Computing: Catch the Wave, PEARC 2020, Virtual, Online, United States, 07/27/20. https://doi.org/10.1145/3311790.3399621

Simulating Large-scale Models of Brain Neuronal Circuits using Google Cloud Platform. / Sivagnanam, Subhashini; Gorman, Wyatt; Doherty, Donald et al.
PEARC 2020 - Practice and Experience in Advanced Research Computing 2020: Catch the Wave. Association for Computing Machinery, 2020. p. 505-509 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - Biophysically detailed modeling provides an unmatched method to integrate data from many disparate experimental studies, and manipulate and explore with high precision the resulting brain circuit simulation. We developed a detailed model of the brain motor cortex circuits, simulating over 10,000 biophysically detailed neurons and 30 million synaptic connections. Optimization and evaluation of the cortical model parameters and responses was achieved via parameter exploration using grid search parameter sweeps and evolutionary algorithms. This involves running tens of thousands of simulations requiring significant computational resources. This paper describes our experience in setting up and using Google Compute Platform (GCP) with Slurm to run these large-scale simulations. We describe the best practices and solutions to the issues that arose during the process, and present preliminary results from running simulations on GCP.

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Sivagnanam S, Gorman W, Doherty D, Neymotin SA, Fang S, Hovhannisyan H et al. Simulating Large-scale Models of Brain Neuronal Circuits using Google Cloud Platform. In PEARC 2020 - Practice and Experience in Advanced Research Computing 2020: Catch the Wave. Association for Computing Machinery. 2020. p. 505-509. (ACM International Conference Proceeding Series). doi: 10.1145/3311790.3399621

Simulating Large-scale Models of Brain Neuronal Circuits using Google Cloud Platform

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Keywords

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