dataesr/scientific-paragraphs-categorization huggingface dataset:
RESULTS The OSB Resource OSB is an online platform (http://www. opensourcebrain. org) that links open source repositories containing standardized models of neurons and circuits to users and developers. OSB provides powerful tools to visualize, analyze, simulate, develop, and test models through web browsers (Figure  1A ). These features were made possible by defining models in the neuroscience model description languages NeuroML  (Cannon et al. , 2014; Gleeson et al. , 2010)  and PyNN  (Davison et al. , 2009). These standardized formats define the properties of models (e. g. , biophysical parameters, cell morphology, and connectivity) in a modular, structured way. This enables model files to be automatically read by OSB and the physiological and anatomical details presented through the browser (Figure  1B ).
They also contain the information required to simulate the model, enabling the functional properties of individual neurons and networks (e. g. , membrane potential and firing activity) to be explored. OSB provides access to metadata associated with the models, including the history of their development (provenance), and has links to wikis, allowing users to discuss their performance and any technical issues. The model code is hosted in public software development repositories (e. g. , on GitHub), because these provide functionality to track and manage changes to the code. This combination of open source software development infrastructure and model standardization enables OSB to deliver up-to-date versions of models in accessible graphical formats (e. g. , 3D views of cells and circuits, tables, and interactive plots) that can be understood and used by the wider neuroscience community.
, 2014). A number of invertebrate models have also been converted  (Boyle and Cohen, 2008; Fitzhugh, 1961; Hodgkin and Huxley, 1952; Prinz et al. , 2004). At the single-cell level, there are models from the Allen Institute Cell Types Database  (Hawrylycz et al. , 2016)  and the Blue Brain Project  (Markram et al. , 2015)  and reconstructed neuronal morphologies from the NeuroMorpho. Org  (Ascoli et al. , 2007)  and Janelia MouseLight  (Economo et al. , 2016)  databases. In addition to the standardized models presented here, there are a number of other user-contributed models on OSB that are in the process of conversion and curation. This community-driven approach encourages organic growth of models and components on OSB and ensures that the range of models available is determined by the interests of the users of the resource.
Scientists wishing to use the infrastructure for model development, collaboration, testing, and dissemination form the core OSB user group  (Figures 1A and 2B). Figure  S1  provides an overview of the steps required to add a model to OSB and the tools we have developed to facilitate this. Once the model is converted to a standardized format, users are able to use the OSB tools for visualization, automated analysis, and testing to help evaluate the accuracy of their code and minimize errors. This facilitates model refinement by ensuring that the intended behavior is not disrupted after each modification. The OSB infrastructure and associated tools can also support larger scale collaborative projects to build and test more complex models. OSB interacts with other neuroinformatics platforms, enabling content to be shared between resources.
For large-scale networks with a high level of biological detail, such as the recently developed CA1 circuit model  (Bezaire et al. , 2016) , OSB can progressively load parts of the network to speed visualization. For example, visualization of the gross structure of the circuit does not require loading the synaptic connectivity matrix (Figure  4I ). However, this can be loaded in the background if required, enabling the properties of the synaptic connectivity to be visualized (Figures  4J-4L ). These features substantially extend the options available for exploring model structure when compared to the original versions of these models, because this information was buried deep within the specialized code (Fortran, NEST SLI, and NEURON hoc in Figures  4A-4D , 4E-4H, and 4I-4L, respectively). Videos S1 and S2 illustrate interactive exploration on OSB of the models shown in Figures  3  and 4 , respectively.
Functional Properties of Models Revealed through Online Simulation To make the functional properties of models of neurons and circuits more accessible to the wider community, we have developed browser-based simulations on OSB, which remove the requirement to write code. This functionality is enabled by the simulator-independent nature of the standardized formats of models on OSB. Instructions for simulating the model are fed to the OSB server, where the code for running the simulation is automatically generated and executed (typically using the NEURON simulator; see STAR Methods). Short simulations can be run quickly on computing resources provided by the OSB server, and larger scale computations can be easily submitted for execution through the Neuroscience Gateway at the San Diego Supercomputer Center  (Sivagnanam et al.
These can be accessed through a popup console tab (Figure  5A , bottom left), which allows a series of instructions to be copied, pasted, and rerun, as well as direct control of OSB through scripting. The web-based nature of OSB, together with its simulation and management features, make it well suited for demonstrating the principles of neurophysiology in an interactive and accessible format. To this end, we have built a framework for constructing online tutorials that can be used to explain concepts through presentation of figures and simulations. These features enable interactive tutorials and virtual experiments to be constructed that can be used to teach basic concepts in neurophysiology and computational neuroscience without the barrier of having to write code or install specialist simulators.
Adding Models to OSB When a new project is added to OSB in a standardized format, the contributor immediately benefits from the automated visualization, analysis, and simulation to showcase their own model. Standardized formats also aid analysis and comparison of the properties and behavior of the cells from different sources (Figure  S2 ). Although some models are originally developed in standardized formats  (Cayco-Gajic et al. , 2017) , most existing models have been developed and defined in simulator-specific languages  (McDougal et al. , 2017)  and therefore require conversion to NeuroML or PyNN. NeuroML is a widely used standardized model description language that is sufficiently flexible to define a wide range of models in neuroscience  (Cannon et al. , 2014; Gleeson et al. , 2010).
Models defined in NeuroML can be automatically ''read'' and visualized or transformed into the instructions required to run simulations (Table  S2 ). PyNN is a Python-based language for describing models that is compatible with a range of simulators, including NEURON  (Carnevale and Hines, 2006) , NEST  (Gewaltig and Diesmann, 2007) , Brian  (Goodman and Brette, 2008) , and neuromorphic hardware  (Schemmel et al. , 2010). Although PyNN and NeuroML have different approaches to model specification, they are interoperable: networks can be created with PyNN scripts and the structure exported to NeuroML format (e. g. , Figures  4E-4H ) and specific cell models in NeuroML can be used in PyNN scripts and run on supported simulators (STAR Methods). We have developed a range of documentation and tools to facilitate the conversion of models into  NeuroML and PyNN (Figures 1A and 1B).
Creating New Models from Existing Components on OSB A key reason to make models and components available on OSB is that they can be reused and adapted to address new scientific questions. NeuroML software libraries (STAR Methods) can be used to create new models by reusing pre-existing components. To illustrate this, we built new cortical network models with differing biological detail by combining existing components using the tools we have developed for construction and optimization of NeuroML-based models (Figure  S1 ). Linking the resulting model to OSB then enabled the visualization, management, and testing functionality to be used to adapt and refine the models. Based on the connectivity and functional properties of the neocortex, it has been suggested that cortical networks operate in a regime with high excitatory gain, which renders the excitatory subnetwork unstable in the absence of strong feedback inhibition  (Tsodyks et al.
Reimplementation of the spiking network model from  Sadeh et al. (2017)  in PyNN, with adaptive exponential integrate-and-fire (I&F) cells, exhibited increases in the firing when the excitatory drive onto inhibitory cells was decreased (Figure  7B ; cf. Figure  10B  in  Sadeh et al. , 2017) , hence confirming that the paradoxical signature of ISN could also be observed in these I&F networks. To investigate whether ISN responses occur with more realistic conductance-based spiking mechanisms, we reused the combination of membrane conductances from cortical cell models in  Pospischil et al. (2008)  and created single-compartment cell models that matched experimentally recorded behavior of layer 2/3 spiny (putative excitatory) and aspiny (putative inhibitory) cells from the Allen Cell Types Database (STAR Methods; Figures  S3A-S3D ).
(C) Network model in NeuroML consisting of point neurons with voltage-gated membrane conductances from  Pospischil et al. (2008)  that were scaled to match the firing behavior of layer 2/3 spiny (E; red) and aspiny (I; blue) neurons from the Allen Cell Types Database. Network visualization, spike raster, and firing rate plots during a decreased excitatory drive to 90% of inhibitory cells as for (B) are shown. See also Figures  S3A-S3D. (D) Similar network to (C), with 10 of the E cells replaced by detailed layer 2/3 cell model from  Smith et al. (2013; green cells in 3D view). A single screenshot of OSB showing the range of graphical elements that can be used to interactively investigate the behavior of the network. Top two plots on left show membrane potentials from a point E neuron (red) and an I cell (blue) as well as the detailed E cell (green).
OSB's browser-based visualization of models of neurons and circuits and automated analysis of their structural and functional properties provides a wealth of information about model properties that was largely inaccessible to non-specialists. Moreover, by removing the technical barriers of having to write code to run and configure simulations on high-performance computing facilities, OSB's browser-controlled simulation functionality now makes it possible for anyone to explore the behavior of complex models. Advanced users have option of downloading any of the models and using the same standards-based toolchain on their own machines (STAR Methods). Academics can also use the OSB online tutorial building functionality to build interactive teaching resources that combine text and simulations to illustrate diverse neurophysiological phenomena.
Allowing a wider range of users to access such detailed models will facilitate critical evaluation from the wider neuroscience community. OSB is designed so that, when a new project is added by a contributor, they immediately benefit from functionality to showcase their own model (Figure  S1 ). An important aspect of OSB is the provision of infrastructure to facilitate the continuous open source development, refinement, and testing of models. This is enabled through standardized model descriptions, new tools for automatically testing code, and deep integration with the code development platform GitHub, which enables collaborative software development. The OSB Model Validation framework, which uses code-testing methodologies from software engineering, helps ensure model behavior does not change when converted to standardized formats and when updates are made to the code.
This can maintain code quality and consistency, enabling complex models to be kept up to date with new experimental results, without introducing errors. The large battery of tests that have already been applied across models and components by this framework (Table  S2 ) enables an unprecedented level of reproducibility to be obtained on OSB. The fact that model development and testing can be carried out without the need for OSB administrators to get involved will enable the resource to expand in a way that is determined by the user's interests and research goals. Models contributed by individual researchers and labs are complemented with community-developed models, including the Human Brain Project (HBP) HippoCamp initiative, for which our CA1 network conversion (Figures  4I-4L ) is a first major contribution, and the OpenWorm project  (Sarma et al.
For example, reconstructed neuronal morphologies from NeuroMorpho. Org  (Ascoli et al. , 2007)  and the Janelia MouseLight project  (Economo et al. , 2016)  can be automatically converted to NeuroML and visualized through OSB, and these morphologies can become the basis for new models when combined with cell-specific membrane conductances already expressed in NeuroML. The Allen Institute Cell Types Database  (Hawrylycz et al. , 2016)  provides electrophysiological recordings and morphological reconstructions from cells in mouse visual cortex. Biophysically detailed cell models and point neuron models based on these data are present on OSB, and new NeuroML-based models can be generated from the source data (Figures  S3A-S3D ). Models currently being used in the HBP  (Amunts et al. , 2016)  have also been converted, including cell models from the Blue Brain Project's reconstruction of the microcircuitry of rat somatosensory cortex  (Markram et al.
, 2014) will allow easier integration of complex biochemical reactions into NeuroML-based models of neuronal and circuit models. It will also enable greater interoperability between OSB and databases such as BioModels  (Le Novere et al. , 2006)  and the Physiome Model Repository  (Yu et al. , 2011). In addition to these subcellular model specifications, NeuroML is currently being extended to support more high-level, population-based models (e. g. , the Wilson and Cowan model;  Wilson and Cowan, 1972)  which will potentially enable the analysis and simulation on OSB of brain-scale networks, as supported by a number of simulators, such as The Virtual Brain  (Sanz Leon et al. , 2013). Building models from new and existing components currently involves configuring and optimizing the model offline and then loading the code to a repository on GitHub, where OSB can validate and test it prior to visualization, analysis, and simulation.
In order to make this process easier, we plan to combine elements of the user interface of NetPyNE (a Python-based platform for model creation built on top of the NEURON simulator; Dura-  Bernal et al. , 2019)  with OSB to provide an online model construction, optimization, and testing interface. This will be facilitated by the fact that the online visualization interfaces of both OSB and NetPyNE are built using the Geppetto platform  (Cantarelli et al. , 2018; STAR Methods). To further lower the barriers to model creation on OSB, we are also expanding the functionality of the platform to include tighter integration with the experimental data used to build models and test their performance. Standardized formats for experimental data, such as Neurodata without Borders (https://nwb. org), will be crucial for this.
An alternative option for running simulations is enabled through OSB integration with the Neuroscience Gateway  (Sivagnanam et al. , 2013)  (NSG, Figure  1A ). OSB generates the simulator scripts and submits these through the RESTful API of NSG (NSG-R, http://www. nsgportal. org/guide. html). These are then submitted to the Extreme Science and Engineering Discovery Environment (XSEDE) high performance computing resources for execution, where NSG has pre-configured multiple neuronal simulation packages. Users on OSB do not require any account on NSG or XSEDE, and completed simulations appear in the OSB interface in the same way, albeit with more of a delay than running on OSB's own servers. Because NSG provides access to parallel computing resources, OSB users can select NetPyNE as a target simulator, and opt to run network models across up to 256 processors, significantly speeding up the simulation time.
This number can be increased in future as we gauge usage statistics/demand, to make greater use of the thousands of processors available via NSG. Scripts have also been developed which allow modelers to submit NeuroML and PyNN directly to NSG-R, bypassing the web interface (https://github. com/OpenSourceBrain/NSGPortalShowcase). Data generated during simulations launched via OSB can be downloaded from the web interface, but can also be set to automatically be added to the user's Dropbox (https://www. dropbox. com) folder, to enable local analysis of the simulation data. This is enabled by generating an API key on the Dropbox site and adding this to the user's OSB account. Existing tutorials for OSB are listed at http://www. opensourcebrain. org/tutorials. Documentation for those interested in developing and hosting tutorials and making use of the visualization and simulation features of OSB can be found at http://opensourcebrain.
Interactions with neuroinformatics resources The cell models used in the Blue Brain Project rat somatosensory microcircuitry network  (Markram et al. , 2015)  have been made available in the original NEURON format on the Neocortical Microcircuit Collaboration Portal (NMCP)  (Ramaswamy et al. , 2015). We have developed scripts for automatically converting these cells to NeuroML and example models in this format have been made available on http://www. opensourcebrain. org/projects/blue-brain-project-showcase. We have also converted the ion channel models from the Channelpedia  (Ranjan et al. , 2011)  database to NeuroML format in this OSB repository. Representative connectomes with point neurons used in recent studies of the Blue Brain Project microcircuit  (Gal et al. , 2017; Reimann et al.
, 2017)  are available on the NMCP and a NeuroML HDF5 based version of this full connectome can be found on the OSB project. The full network (31346 cells, 7. 6 million connections) cannot yet be displayed on OSB (though this is a target for future releases). However a scaled down version, with 5% of neurons in the original is available and can be visualized. The Allen Cell Types Database  (Hawrylycz et al. , 2016)  (http://celltypes. brain-map. org) contains neuronal reconstructions and electrophysiological recordings from multiple cells in mouse visual cortex (electrophysiological data samples shown in Figures  S3A  and S3C , see below). Computational models of these cells are also available on the website in both biophysically and morphologically detailed (implemented in NEURON simulator) and point neuron (Generalized Linear Integrate and Fire (GLIF) models in a custom Python simulator) formats.
These have both been converted to NeuroML/LEMS formats on http://www. opensourcebrain. org/projects/alleninstituteneuroml. Each cell shown in Figure  3A  has a unique complement of ionic conductance densities tuned to the original cell's electrophysiological recordings. Neuronal reconstructions from http://neuromorpho. org/ (Ascoli et al. , 2007)  are available in standardized SWC format  (Cannon et al. , 1998)  and these files, containing information on the 3D locations, radii, connectivity, and type of the points in the reconstruction, can be visualized directly on OSB by placing them into a GitHub repository (see http://www. opensourcebrain. org/projects/ neuromorpho). These files can be converted to NeuroML, to build them into spiking neuron models with active conductances, by using the application at https://github.
com/pgleeson/Cvapp-NeuroMorpho. org or by loading the SWC into neuroConstruct  (Gleeson et al. , 2007) , editing the cells and exporting to NeuroML. The Janelia MouseLight project  (Economo et al. , 2016)  provides SWC versions of their neuronal reconstructions, in addition to a proprietary JSON file format with extra metadata. The OSB project for this (http://www. opensourcebrain. org/projects/mouselightshowcase) provides scripts for converting the JSON files to NeuroML while retaining the metadata. For both the NeuroMorpho. Org and MouseLight repositories, a small set of example cell files have been converted to NeuroML and added for visualization on OSB, and instructions/scripts are included for converting any other cells from those resources to NeuroML.
Testing and model validation Model development requires systematic testing of the code base. In software engineering, it is standard practice to run automated tests whenever a change is made to the code and it is committed to a repository (''continuous integration''). To facilitate this on OSB we have developed the OSB Model Validation framework (OMV, https://github. com/OpenSourceBrain/osb-model-validation), which e3 Neuron 103, 395-411. e1-e5, August 7, 2019 allows test configuration files to be added to the repository for an OSB project. Expected behaviors (e. g. , spike times, resting membrane potential), as well as consistency checks for parameters such as total membrane area and temperature are described in short Model Emergent Properties (mep) files. Then a number of OSB Model Test (omt) files are written for each of the simulator configurations (engines) which should produce that behavior, specifying the allowed tolerance.
1 nS and 0. 2 nS, and the reversal potentials for excitation and inhibition were 0 mV and À75 mV, respectively. The connection strength for background input was 0. 1 nS. To detect ISN properties of the network, the baseline activity of the network was perturbed by changing the input to a fraction of inhibitory neurons. The perturbation was performed by reducing the baseline input to perturbed inhibitory neurons by 400 Hz (i. e. , by $4%) and was repeated for multiple trials to obtain average firing rates before and after perturbation. For further details on the model, including detailed properties of neurons and connectivity, see  Sadeh et al. , 2017. Code is available in the PyNN subfolder of https://github. com/OpenSourceBrain/SadehEtAl2017-InhibitionStabilizedNetworks. The model was exported to NeuroML from PyNN to generate the 3D view on OSB.
In order to create more realistic conductance-based, ISN models, the exponential integrate-and-fire neuron model in Figure  7B  was replaced with different neuron types in Figure  7C , while keeping other properties of the network connectivity and stimulation protocol similar. To this end, Hodgkin-Huxley type point neurons with voltage-gated membrane conductances from  Pospischil et al. (2008)  were scaled to match the firing behavior of layer 2/3 spiny (E, red) and aspiny (I, blue) neurons from the Allen Cell Types Database. Scripts were created to download electrophysiological data from mouse visual cortex neurons from the Allen Cell Types Database (subfolder CellTypesDatabase/data on https://github. com/OpenSourceBrain/AllenInstituteNeuroML). From membrane potential traces of cells receiving 1 s current pulse stimuli (examples shown in Figures  S3A  and S3C ), information was extracted on input stimuli information, spike times, spike height, subthreshold steady states and these values were used to retune the model of  Pospischil et al.