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Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds
In this study, we explore the simulation setup in computational neuroscience. We use GENESIS, a general purpose simulation engine for sub-cellular components and biochemical reactions, realistic neuron models, large neural networks, and system-level models. GENESIS supports developing and running co...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10070792/ https://www.ncbi.nlm.nih.gov/pubmed/37025550 http://dx.doi.org/10.3389/fninf.2023.1122470 |
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author | Chlasta, Karol Sochaczewski, Paweł Wójcik, Grzegorz M. Krejtz, Izabela |
author_facet | Chlasta, Karol Sochaczewski, Paweł Wójcik, Grzegorz M. Krejtz, Izabela |
author_sort | Chlasta, Karol |
collection | PubMed |
description | In this study, we explore the simulation setup in computational neuroscience. We use GENESIS, a general purpose simulation engine for sub-cellular components and biochemical reactions, realistic neuron models, large neural networks, and system-level models. GENESIS supports developing and running computer simulations but leaves a gap for setting up today's larger and more complex models. The field of realistic models of brain networks has overgrown the simplicity of earliest models. The challenges include managing the complexity of software dependencies and various models, setting up model parameter values, storing the input parameters alongside the results, and providing execution statistics. Moreover, in the high performance computing (HPC) context, public cloud resources are becoming an alternative to the expensive on-premises clusters. We present Neural Simulation Pipeline (NSP), which facilitates the large-scale computer simulations and their deployment to multiple computing infrastructures using the infrastructure as the code (IaC) containerization approach. The authors demonstrate the effectiveness of NSP in a pattern recognition task programmed with GENESIS, through a custom-built visual system, called RetNet(8 × 5,1) that uses biologically plausible Hodgkin–Huxley spiking neurons. We evaluate the pipeline by performing 54 simulations executed on-premise, at the Hasso Plattner Institute's (HPI) Future Service-Oriented Computing (SOC) Lab, and through the Amazon Web Services (AWS), the biggest public cloud service provider in the world. We report on the non-containerized and containerized execution with Docker, as well as present the cost per simulation in AWS. The results show that our neural simulation pipeline can reduce entry barriers to neural simulations, making them more practical and cost-effective. |
format | Online Article Text |
id | pubmed-10070792 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-100707922023-04-05 Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds Chlasta, Karol Sochaczewski, Paweł Wójcik, Grzegorz M. Krejtz, Izabela Front Neuroinform Neuroscience In this study, we explore the simulation setup in computational neuroscience. We use GENESIS, a general purpose simulation engine for sub-cellular components and biochemical reactions, realistic neuron models, large neural networks, and system-level models. GENESIS supports developing and running computer simulations but leaves a gap for setting up today's larger and more complex models. The field of realistic models of brain networks has overgrown the simplicity of earliest models. The challenges include managing the complexity of software dependencies and various models, setting up model parameter values, storing the input parameters alongside the results, and providing execution statistics. Moreover, in the high performance computing (HPC) context, public cloud resources are becoming an alternative to the expensive on-premises clusters. We present Neural Simulation Pipeline (NSP), which facilitates the large-scale computer simulations and their deployment to multiple computing infrastructures using the infrastructure as the code (IaC) containerization approach. The authors demonstrate the effectiveness of NSP in a pattern recognition task programmed with GENESIS, through a custom-built visual system, called RetNet(8 × 5,1) that uses biologically plausible Hodgkin–Huxley spiking neurons. We evaluate the pipeline by performing 54 simulations executed on-premise, at the Hasso Plattner Institute's (HPI) Future Service-Oriented Computing (SOC) Lab, and through the Amazon Web Services (AWS), the biggest public cloud service provider in the world. We report on the non-containerized and containerized execution with Docker, as well as present the cost per simulation in AWS. The results show that our neural simulation pipeline can reduce entry barriers to neural simulations, making them more practical and cost-effective. Frontiers Media S.A. 2023-03-21 /pmc/articles/PMC10070792/ /pubmed/37025550 http://dx.doi.org/10.3389/fninf.2023.1122470 Text en Copyright © 2023 Chlasta, Sochaczewski, Wójcik and Krejtz. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neuroscience Chlasta, Karol Sochaczewski, Paweł Wójcik, Grzegorz M. Krejtz, Izabela Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds |
title | Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds |
title_full | Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds |
title_fullStr | Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds |
title_full_unstemmed | Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds |
title_short | Neural simulation pipeline: Enabling container-based simulations on-premise and in public clouds |
title_sort | neural simulation pipeline: enabling container-based simulations on-premise and in public clouds |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10070792/ https://www.ncbi.nlm.nih.gov/pubmed/37025550 http://dx.doi.org/10.3389/fninf.2023.1122470 |
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