A Data-Driven Biophysical Computational Model of Parkinson’s Disease Based on Marmoset Monkeys

Ranieri, Caetano Mazzoni; Pimentel, Jhielson M.; Romano, M.R.; Elias, Leonardo Abdala; Romero, Roseli Aparecida Francelin; Lones, Michael A.; Araújo, Mariana Ferreira Pereira de; Vargas, Patrícia A.; Moioli, Renan Cipriano

doi:10.1109/access.2021.3108682

Cited by 9 publications

(8 citation statements)

References 106 publications

(143 reference statements)

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“…NetPyNE (Dura-Bernal et al, 2019 ) is a high-level declarative NEURON wrapper used to develop a wide range of neural circuit models (Metzner et al, 2020 ; Anwar et al, 2021 ; Bryson et al, 2021 ; Pimentel et al, 2021 ; Ranieri et al, 2021 ; Romaro et al, 2021 ; Volk et al, 2021 ; Borges et al, 2022 ; Dura-Bernal et al, 2022a , b ; Medlock et al, 2022 ) 4 , and also as a resource for teaching neurobiology and computational neuroscience.…”

Section: Methodsmentioning

confidence: 99%

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

Awile

Kumbhar

Cornu

et al. 2022

Front. Neuroinform.

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The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON's ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON's reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.

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Section: Methodsmentioning

confidence: 99%

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

Awile

Kumbhar

Cornu

et al. 2022

Front. Neuroinform.

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“…The S1 model now joins other NetPyNE cortical simulations: generic cortical circuits (Romaro et al 2021), auditory and motor thalamocortical circuits (Sivagnanam et al 2020;Dura-Bernal, Griffith, et al 2022), as well as simulations of thalamus (Moreira et al 2021), dorsal horn of spinal cord (Sekiguchi et al 2021), Parkinson's disease (Ranieri et al 2021) and schizophrenia (Metzner et al 2020). These large cortical simulations can be extremely computer-intensive, which is a major motivation for NetPyNE's facilities that allow one to readily simplify the network by swapping in integrate-and-fire or small-compartmental cell models, or by down-scaling to more manageable sizes.…”

Section: Discussionmentioning

confidence: 99%

“…Our new port of the S1 model provides a quantitative framework that can be used in several ways. First, it can be used to perform in silico experiments to explore somatosensory processing under the assumption of various coding paradigms or brain disease (Amsalem et al 2020; Metzner et al 2020; Ranieri et al 2021). Second, drug effects can be directly tested in the simulation (Neymotin et al 2016) -- this is an advantage of a multiscale model with scales from molecule to network, which is not available in simpler models that elide these details.…”

Section: Discussionmentioning

confidence: 99%

Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE

Borges

Moreira

Takarabe

et al. 2022

Preprint

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The primary somatosensory cortex (S1) of mammals is critically important in the perception of touch and related sensorimotor behaviors. In 2015 the Blue Brain Project developed a groundbreaking rat S1 microcircuit simulation with over 31,000 neurons with 207 morpho-electrical neuron types, and 37 million synapses, incorporating anatomical and physiological information from a wide range of experimental studies. We have implemented this highly-detailed and complex model S1 model in NetPyNE, using the data available in the Neocortical Microcircuit Collaboration Portal. NetPyNE provides a Python high-level interface to NEURON and allows defining complicated multiscale models using an intuitive declarative standardized language. It also facilitates running parallel simulations, automates the optimization and exploration of parameters using supercomputers, and provides a wide range of built-in analysis functions. This will make the S1 model more accessible and simpler to scale, modify and extend in order to explore research questions or interconnect to other existing models. Despite some implementation differences, the NetPyNE model closely reproduced the original cell morphologies, electrophysiological responses, spatial distribution for all 207 cell types; and the connectivity properties of the 1941 pathways, including synaptic dynamics and short-term plasticity (STP). The NetPyNE S1 simulations produced reasonable physiological rates and activity patterns across all populations, and, when STP was included, produced a 1 Hz oscillation comparable to that of the original model. We also extended the model by adding thalamic circuits, including 6 distinct thalamic populations with intrathalamic, thalamocortical and corticothalamic connectivity derived from experimental data. Overall, our work provides a widely accessible, data-driven and biophysically-detailed model of the somatosensory thalamocortical circuits that can be utilized as a community tool for researchers to study neural dynamics, function and disease.

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“…NetPyNE (Dura-Bernal et al, 2019) is a high-level declarative NEURON wrapper used to develop a wide range of neural circuit models (Bryson et al, 2021; Pimentel et al, 2021; Volk et al, 2021; Ranieri et al, 2021; Metzner et al, 2020; Anwar et al, 2021; Sekiguchi et al, 2021; Dura-Bernal et al, 2022a,b; Borges et al, 2022; Romaro et al, 2021) 4 , and also as a resource for teaching neurobiology and computational neuroscience.…”

Section: Methodsmentioning

confidence: 99%

“…When running on a GPU, however, one must use the special executable to launch simulations due to limitations of the NVIDIA compiler toolchain when using OpenACC together with shared libraries. (Bryson et al, 2021;Pimentel et al, 2021;Volk et al, 2021;Ranieri et al, 2021;Metzner et al, 2020;Anwar et al, 2021;Sekiguchi et al, 2021;Dura-Bernal et al, 2022a,b;Borges et al, 2022;Romaro et al, 2021) 4 , and also as a resource for teaching neurobiology and computational neuroscience.…”

Section: Integration Of Code Generation Pipelinesmentioning

confidence: 99%

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

Awile

Kumbhar

Cornu

et al. 2022

Preprint

View full text Add to dashboard Cite

The need for reproducible, credible, multiscale biological modeling has led to the development of standardized simulation platforms, such as the widely-used NEURON environment for computational neuroscience. Developing and maintaining NEURON over several decades has required attention to the competing needs of backwards compatibility, evolving computer architectures, the addition of new scales and physical processes, accessibility to new users, and efficiency and flexibility for specialists. In order to meet these challenges, we have now substantially modernized NEURON, providing continuous integration, an improved build system and release workflow, and better documentation. With the help of a new source-to-source compiler of the NMODL domain-specific language we have enhanced NEURON’s ability to run efficiently, via the CoreNEURON simulation engine, on a variety of hardware platforms, including GPUs. Through the implementation of an optimized in-memory transfer mechanism this performance optimized backend is made easily accessible to users, providing training and model-development paths from laptop to workstation to supercomputer and cloud platform. Similarly, we have been able to accelerate NEURON’s reaction-diffusion simulation performance through the use of just-in-time compilation. We show that these efforts have led to a growing developer base, a simpler and more robust software distribution, a wider range of supported computer architectures, a better integration of NEURON with other scientific workflows, and substantially improved performance for the simulation of biophysical and biochemical models.

show abstract

A Data-Driven Biophysical Computational Model of Parkinson’s Disease Based on Marmoset Monkeys

Cited by 9 publications

References 106 publications

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE

Modernizing the NEURON Simulator for Sustainability, Portability, and Performance

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