The SONATA Data Format for Efficient Description of Large-Scale Network Models

Dai, Kael; Hernando, Juan; Billeh, Yazan N.; Gratiy, Sergey L.; Planas, Judit; Davison, Andrew; Durá-Bernal, Salvador; Gleeson, Padraig; Devresse, Adrien; Dichter, Benjamin; Gevaert, Michael; King, James G.; Geit, Werner A. H. Van; Povolotsky, Arseny V.; Müller, Eilif; Courcol, Jean-Denis; Arkhipov, Anton

doi:10.1101/625491

Cited by 8 publications

(8 citation statements)

References 47 publications

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“…The models were constructed and simulated using the Brain Modeling ToolKit (BMTK; https://alleninstitute.github.io/bmtk/) interfaced with NEST (Peyser et al 2017) and utilized the SONATA modeling format (https://github.com/AllenInstitute/sonata) (Dai et al 2019). These tools and our simulation results are publicly available as a free resource for the community.…”

Section: Introductionmentioning

confidence: 99%

Modeling robust and efficient coding in the mouse primary visual cortex using computational perturbations

Cai

Billeh

Chettih

et al. 2020

Preprint

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Investigating how visual inputs are encoded in visual cortex is important for elucidating the roles of cell populations in circuit computations. We here use a recently developed, large-scale model of mouse primary visual cortex (V1) and perturb both single neurons as well as functional-and cell-type defined population of neurons to mimic equivalent optogenetic perturbations. First, perturbations were performed to study the functional roles of layer 2/3 excitatory neurons in inter-laminar interactions. We observed activity changes consistent with the canonical cortical model (Douglas and Martin 1991). Second, single neuron perturbations in layer 2/3 revealed a center-surround inhibition-dominated effect, consistent with recent experiments. Finally, perturbations of multiple excitatory layer 2/3 neurons during visual stimuli of varying contrasts indicated that the V1 model has both efficient and robust coding features. The circuit transitions from predominantly broad like-to-like inhibition at high contrasts to predominantly specific liketo-like excitation at low contrasts. These in silico results demonstrate how the circuit can shift from redundancy reduction to robust codes as a function of stimulus contrast.

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Section: Introductionmentioning

confidence: 99%

Modeling robust and efficient coding in the mouse primary visual cortex using computational perturbations

Cai

Billeh

Chettih

et al. 2020

Preprint

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“…Power spectral density (PSD) of LFP and firing rates during spontaneous activity A. LFP PSDs at different depths for base model simulation: peaks were in high beta / low gamma range(28)(29)(30)(31)(32)(33)(34)(35). B. LFP PSDs across full model set (N=25) at upper L5B with mean (blue line) and interquartile ranges (bars).…”

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confidence: 99%

Multiscale dynamics and information flow in a data-driven model of the primary motor cortex microcircuit

Durá-Bernal

Neymotin

Suter

et al. 2017

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We developed a biologically detailed multiscale model of mouse primary motor cortex (M1) microcircuits, incorporating data from several recent experimental studies. The model simulates at scale a cylindrical volume with a diameter of 300 m and cortical depth 1350 m of M1. It includes over 10,000 cells distributed across cortical layers based on measured cell densities, with close to 30 million synaptic connections. Neuron models were optimized to reproduce electrophysiological properties of major classes of M1 neurons. Layer 5 corticospinal and corticostriatal neuron morphologies with 700+ compartments reproduced cell 3D reconstructions, and their ionic channel distributions were optimized within experimental constraints to reproduce in vitro recordings. The network was driven by the main long-range inputs to M1: posterior nucleus (PO) and ventrolateral (VL) thalamus PO, primary and secondary somatosensory cortices (S1, S2), contralateral M1, secondary motor cortex (M2), and orbital cortex (OC). The network local and long-range connections depended on pre-and post-synaptic cell class and cortical depth. Data was based on optogenetic circuit mapping studies which determined that connection strengths vary within layer as a function of the neuron's cortical depth. The synaptic input distribution across cell dendritic trees -likely to subserve important neural coding functions -was also mapped using optogenetic methods and incorporated into the model. We employed the model to study the effect on M1 of increased activity from each of the long-range inputs, and of different levels of H-current in pyramidal tract-projecting (PT) corticospinal neurons. Microcircuit dynamics and information flow were quantified using firing rates, oscillations, and information transfer measures (Spectral Granger causality). We evaluated the response to short pulses and long time-varying activity arising from the different long-range inputs. We also studied the interaction between two simultaneous long-range inputs. Simulation results support the hypothesis that M1 operates along a continuum of modes characterized by the degree of activation of intratelencephalic (IT), corticothalamic (CT) and PT neurons. The particular subset of M1 neurons targeted by different long-range inputs and the level of H-current in PT neurons regulated the different modes. Downregulation of H-current facilitated synaptic integration of inputs and increased PT output activity. Therefore, VL, cM1 and M2 inputs with downregulated H-current promoted an PT-predominant mode; whereas PO, S11 . CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/201707 doi: bioRxiv preprint first posted online Oct. 11, 2017; and S2 inputs with upregulated H-current favored an IT-predominant mode, but a mixed mode where upper layer IT cells in turn activated PT cells if H-c...

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“…The catalog also includes the ndx-simulation-output extension for the storage of the outputs of large-scale simulations. Large-scale network models that are described using the new SONATA format 38 can be converted to NWB using this extension.…”

Section: Resultsmentioning

confidence: 99%

The Neurodata Without Borders ecosystem for neurophysiological data science

Rübel

Tritt

et al. 2021

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The neurophysiology of cells and tissues are monitored electrophysiologically and optically in diverse experiments and species, ranging from flies to humans. Understanding the brain requires integration of data across this diversity, and thus these data must be findable, accessible, interoperable, and reusable (FAIR). This requires a standard language for data and metadata that can coevolve with neuroscience. We describe design and implementation principles for a language for neurophysiology data. Our software (Neurodata Without Borders, NWB) defines and modularizes the interdependent, yet separable, components of a data language. We demonstrate NWB's impact through unified description of neurophysiology data across diverse modalities and species. NWB exists in an ecosystem which includes data management, analysis, visualization, and archive tools. Thus, the NWB data language enables reproduction, interchange, and reuse of diverse neurophysiology data. More broadly, the design principles of NWB are generally applicable to enhance discovery across biology through data FAIRness.

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The SONATA Data Format for Efficient Description of Large-Scale Network Models

Cited by 8 publications

References 47 publications

Modeling robust and efficient coding in the mouse primary visual cortex using computational perturbations

Modeling robust and efficient coding in the mouse primary visual cortex using computational perturbations

Multiscale dynamics and information flow in a data-driven model of the primary motor cortex microcircuit

The Neurodata Without Borders ecosystem for neurophysiological data science

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