Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits

Gleeson, Padraig; Carandini, Matteo; Marin, Bóris; Quintana, Adrián; Earnshaw, Matt; Piasini, Eugenio; Birgiolas, Justas; Cannon, Robert C.; Cayco-Gajic, N. Alex; Crook, Sharon; Davison, Andrew; Durá-Bernal, Salvador; Ecker, András; Hines, Michael L.; Idili, Giovanni; Larson, Stephen; Lytton, William W.; Majumdar, Amitava; McDougal, Robert A.; Sivagnanam, Subhashini; Solinas, Sergio; Stanislovas, Rokas; Albada, Sacha J. van; Geit, Werner Van; Silver, R. Angus

doi:10.1101/229484

Cited by 15 publications

(17 citation statements)

References 65 publications

(91 reference statements)

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“…The Open Source Brain platform is a web resource for publishing and sharing models in the field of computational neuroscience with a strong focus on open source technologies. The submitted models can be visualized and their parameter spaces and dynamics can be explored in browser-based simulations (Gleeson et al, 2018 ).…”

Section: Resultsmentioning

confidence: 99%

Reproducing Polychronization: A Guide to Maximizing the Reproducibility of Spiking Network Models

Pauli

Weidel

Kunkel

et al. 2018

Front. Neuroinform.

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Any modeler who has attempted to reproduce a spiking neural network model from its description in a paper has discovered what a painful endeavor this is. Even when all parameters appear to have been specified, which is rare, typically the initial attempt to reproduce the network does not yield results that are recognizably akin to those in the original publication. Causes include inaccurately reported or hidden parameters (e.g., wrong unit or the existence of an initialization distribution), differences in implementation of model dynamics, and ambiguities in the text description of the network experiment. The very fact that adequate reproduction often cannot be achieved until a series of such causes have been tracked down and resolved is in itself disconcerting, as it reveals unreported model dependencies on specific implementation choices that either were not clear to the original authors, or that they chose not to disclose. In either case, such dependencies diminish the credibility of the model's claims about the behavior of the target system. To demonstrate these issues, we provide a worked example of reproducing a seminal study for which, unusually, source code was provided at time of publication. Despite this seemingly optimal starting position, reproducing the results was time consuming and frustrating. Further examination of the correctly reproduced model reveals that it is highly sensitive to implementation choices such as the realization of background noise, the integration timestep, and the thresholding parameter of the analysis algorithm. From this process, we derive a guideline of best practices that would substantially reduce the investment in reproducing neural network studies, whilst simultaneously increasing their scientific quality. We propose that this guideline can be used by authors and reviewers to assess and improve the reproducibility of future network models.

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Section: Resultsmentioning

confidence: 99%

Reproducing Polychronization: A Guide to Maximizing the Reproducibility of Spiking Network Models

Pauli

Weidel

Kunkel

et al. 2018

Front. Neuroinform.

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“…Until we have comprehensive predictive models, engineers will likely rely on ad hoc combinations of predictive models of parts of organisms, data-driven models, and heuristic design rules. BioModels [74], the NeuroML database [75], Open Source Brain [76], and the Physiome Model Repository [77] already contain hundreds of models of parts of organisms that can be used to help design organisms. For example, metabolic engineers often use constraint-based models to design microbial factories [78].…”

Section: Learning Systematically From Test Resultsmentioning

confidence: 99%

Organizing genome engineering for the gigabase scale

et al. 2020

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Engineering the entire genome of an organism enables large-scale changes in organization, function, and external interactions, with significant implications for industry, medicine, and the environment. Improvements to DNA synthesis and organism engineering are already enabling substantial changes to organisms with megabase genomes, such as Escherichia coli and Saccharomyces cerevisiae. Simultaneously, recent advances in genome-scale modeling are increasingly informing the design of metabolic networks. However, major challenges remain for integrating these and other relevant technologies into workflows that can scale to the engineering of gigabase genomes.In particular, we find that a major under-recognized challenge is coordinating the flow of models, designs, constructs, and measurements across the large teams and complex technological systems that will likely be required for gigabase genome engineering. We recommend that the community address these challenges by 1) adopting and extending existing standards and technologies for representing and exchanging information at the gigabase genomic scale, 2) developing new technologies to address major open questions around data curation and quality control, 3) conducting fundamental research on the integration of modeling and design at the genomic scale, and 4) developing new legal and contractual infrastructure to better enable collaboration across multiple institutions.

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“…Although primarily designed for guidance in system experimentation, these visualization tools have significantly improved the access to and exploration of brain data. A number of efforts started to bridge the gap between neurobiological data and computational modeling including the Geppetto [8], the OpenWorm [58] and the Open Source Brain [23] initiatives and the Brain Simulation Platform of the Human Brain Project [17].…”

Section: Discussionmentioning

confidence: 99%

FlyBrainLab: Accelerating the Discovery of the Functional Logic of theDrosophilaBrain in the Connectomic/Synaptomic Era

Lazar¹,

Liu²,

Türkcan³

et al. 2020

Preprint

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In recent years, a wealth of Drosophila neuroscience data have become available. These include cell type, connectome and synaptome datasets for both the larva and adult fly. To facilitate integration across data modalities and to accelerate the understanding of the functional logic of the fly brain, we developed an interactive computing environment called FlyBrainLab.FlyBrainLab is uniquely positioned towards accelerating the discovery of the functional logic of the Drosophila brain. Its interactive open source architecture seamlessly integrates and brings together computational models with neuroanatomical, neurogenetic and electrophysiological data, changing the organization of neuroscientific fly brain data from a group of unconnected databases, arrays and tables, to a well structured data and executable circuit repository. The FlyBrainLab User Interface supports a highly intuitive and automated workflow that streamlines the 3D exploration and visualization of fly brain circuits, and the interactive exploration of the functional logic of executable circuits created directly from the explored and visualized fly brain data.FlyBrainLab methodologically supports the efficient comparison of fly brain circuit models, across model instances developed by different researchers, across different developmental stages of the fruit fly and across different datasets. The FlyBrainLab Utility and Circuit Libraries accelerate the creation of models of executable circuits. The Utility Libraries help untangle the graph structure of neural circuits from raw connectome and synaptome data. The Circuit Libraries facilitate the exploration of neural circuits of the neuropils of the central complex and, the development and implementation of models of the adult and larva fruit fly early olfactory systems.To elevate its executable circuit construction capability beyond the connectome, FlyBrainLab provides additional libraries for molecular transduction arising in sensory coding in vision and olfaction. Together with sensory neuron activity data, these libraries serve as entry points for discovering circuit function in the sensory systems of the fruit fly brain. They also enable the biological validation of developed executable circuits within the same platform.

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Open Source Brain: a collaborative resource for visualizing, analyzing, simulating and developing standardized models of neurons and circuits

Cited by 15 publications

References 65 publications

Reproducing Polychronization: A Guide to Maximizing the Reproducibility of Spiking Network Models

Reproducing Polychronization: A Guide to Maximizing the Reproducibility of Spiking Network Models

Organizing genome engineering for the gigabase scale

FlyBrainLab: Accelerating the Discovery of the Functional Logic of theDrosophilaBrain in the Connectomic/Synaptomic Era

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