Multiscale model of primary motor cortex circuits predicts in vivo cell type-specific, behavioral state-dependent dynamics

Durá-Bernal, Salvador; Neymotin, Samuel A.; Suter, Benjamin A.; Dacre, Joshua; Schiemann, Julia; Duguid, Ian; Shepherd, Gordon M.; Lytton, William W.

doi:10.1101/2022.02.03.479040

Cited by 11 publications

(16 citation statements)

References 152 publications

(376 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, Neymotin, et al 2022; 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%

“…NetPyNE translates these specifications into a NEURON model, facilitates running parallel simulations, automates the optimization and exploration of parameters using supercomputers, and provides a wide range of built-in analysis functions. Conversion to NetPyNE also makes it easier to connect to previous models developed within the platform, such as our primary motor cortex model (Sivagnanam et al 2020; Dura-Bernal, Neymotin, et al 2022), and models implemented in other tools (e.g. NEST) by exporting to the NeuroML or SONATA standard formats.…”

Section: Introductionmentioning

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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“…Generating physiologically constrained firing rates in all model populations required parameter tuning (also referred to as parameter fitting or optimization) of the connection strengths within biologically realistic ranges. When compared to our previous motor cortex model (Sivagnanam et al 2020; Dura-Bernal et al 2022), this process was particularly challenging in the NHP auditory system model, and required developing and iteratively improving our automated parameter optimization methods. We believe the reasons for this include the addition of two inhibitory cell types (VIP, NGF) and the incorporation of thalamic circuitry, which resulted in complex recurrent intracortical and thalamocortical interactions.…”

Section: Discussionmentioning

confidence: 99%

Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics

Durá-Bernal

Griffith

Barczak

et al. 2022

Preprint

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We developed a biophysically-detailed model of the macaque auditory thalamocortical circuits, including primary auditory cortex (A1), medial geniculate body (MGB) and thalamic reticular nuclei (TRN), using the NEURON simulator and NetPyNE multiscale modeling tool. We simulated A1 as a cortical column with a depth of 2000 μm and 200 μm diameter, containing over 12k neurons and 30M synapses. Neuron densities, laminar locations, classes, morphology and biophysics, and connectivity at the long-range, local and dendritic scale were derived from published experimental data. The A1 model included 6 cortical layers and multiple populations of neurons consisting of 4 excitatory and 4 inhibitory types, and was reciprocally connected to the thalamus (MGB and TRN) mimicking anatomical connectivity. MGB included core and matrix thalamocortical neurons with layer-specific projection patterns to A1, and thalamic interneurons projecting locally. Auditory stimulus related inputs were simulated using phenomenological models of the cochlear auditory nerve and the inferior colliculus, which served as input to MGB. The model generated cell type and layer-specific firing rates consistent with overall ranges observed experimentally, and accurately simulated the corresponding local field potentials (LFPs), current source density (CSD), and electroencephalogram (EEG) signals. Laminar CSD patterns during spontaneous activity and responses to speech input were similar to those recorded experimentally. Physiological oscillations emerged spontaneously across frequency bands without external rhythmic inputs and were comparable to those recorded in vivo. We used the model to unravel the contributions from distinct cell type and layer-specific neuronal populations to oscillation events detected in CSD, and explore how these relate to the population firing patterns. Overall, the computational model provides a quantitative theoretical framework to integrate and interpret a wide range of experimental data in auditory circuits. It also constitutes a powerful tool to evaluate hypotheses and make predictions about the cellular and network mechanisms underlying common experimental measurements, including MUA, LFP and EEG signals.

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Multiscale model of primary motor cortex circuits predicts in vivo cell type-specific, behavioral state-dependent dynamics

Cited by 11 publications

References 152 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

Data-driven multiscale model of macaque auditory thalamocortical circuits reproduces in vivo dynamics

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