Computational models are powerful tools for exploring the properties of complex biological systems. In neuroscience, data-driven models of neural circuits that span multiple scales are increasingly being used to understand brain function in health and disease. But their adoption and reuse has been limited by the specialist knowledge required to evaluate and use them. To address this, we have developed Open Source Brain, a platform for sharing, viewing, analyzing, and simulating standardized models from different brain regions and species. Model structure and parameters can be automatically visualized and their dynamical properties explored through browser-based simulations. Infrastructure and tools for collaborative interaction, development, and testing are also provided. We demonstrate how existing components can be reused by constructing new models of inhibition-stabilized cortical networks that match recent experimental results. These features of Open Source Brain improve the accessibility, transparency, and reproducibility of models and facilitate their reuse by the wider community.

To address these challenges, we have developed Open Source Brain (OSB) ( http://www.opensourcebrain.org ), a web-based collaborative resource for viewing, simulating, disseminating, and developing standardized models of neurons and circuits. OSB hosts a range of neuronal and circuit models from multiple brain regions, including the neocortex, cerebellum, and hippocampus. The morphology of modeled neurons, the structure and connectivity of networks, and the values of physiological parameters used can be automatically visualized in graphical form on OSB through a web browser. Moreover, functional properties can be explored by simulating models through the browser without installing programs or writing code. Unlike previous repositories, deep links between OSB and GitHub provide a collaborative resource for developing, refining, and automatically testing models, enabling them to evolve as new information becomes available. OSB functionality has been achieved by combining tools and best practices from the open source software development community, harnessing modern web technologies, and integrating them with standardized modular descriptions of models (). Using OSB and associated offline tools, it is possible to combine model components across different levels of biological detail. We illustrate this functionality by constructing models of multiscale inhibition-stabilized cortical networks (ISNs) () and analyzing their robustness to different levels of biological detail. By making models more accessible and facilitating model development through collaboration, OSB provides an online resource of standardized models that can be critically evaluated and reused by the wider neuroscience community.

Ensuring biologically detailed models are robust and error free is challenging given the length and complexity of their software implementations. Common errors include typos in equation definitions and parameter values, unit conversions, inconsistent use of temperature dependencies, and incorrect translation of reconstructed neuronal morphologies. In industry, open source software development is increasingly being used to create well-tested, modular software components and applications, which can be shared publicly using code development and collaboration platforms, such as GitHub (). GitHub records changes in the code and allows multiple users to manage and recombine different versions, track issues, and flag stable versions of the code. Errors can be minimized by regularly testing each modular component with automated routines and then assembling them into larger structures. In computational neuroscience, standardized modular frameworks (“model description languages”) have also been developed for specifying the biological components of circuits, such as ionic and synaptic conductances, neuronal morphologies, and synaptic connectivity (). These components could be used to build modular models that are easy to configure and test, facilitating their reuse for different scientific questions. However, adoption of strategies currently used in open source software engineering for creating, managing, testing, and validating modular code has been the exception rather than the rule for neural modeling in academia ().

Constructing well-constrained models of neurons and circuits from a raw dataset takes a considerable amount of time and skill despite well-established simulation tools (). Once built, the complexity of detailed models makes them difficult to modify for new scientific questions. Moreover, running large-scale circuit models often requires high-performance computing facilities, which may not be accessible to many end users and brings an additional layer of difficulty to setting up simulations and managing the resultant datasets. These technical barriers hinder access to the structural and functional properties of biologically detailed models, limiting scientific scrutiny and adoption of these powerful tools by the wider community.

Models range widely in their level of biological detail, ranging from reduced “top-down” models that provide insights into high-level dynamical behavior of circuits to biologically detailed “bottom-up” models () that enable investigation of the mechanisms underlying circuit function. Biologically detailed circuit models are necessarily complex and typically have a large number of parameters. Experimental measurements from connectomics (), functional activity mapping (), and multi-cell and automated patch clamping (), together with datasets from large-scale brain initiatives (), are providing an increasingly wide range of data to constrain such models, thereby improving their accuracy. But confidence in the predictions from biologically detailed models is currently limited by their complexity and their perceived lack of constraint.

Computational modeling is a powerful approach for investigating and understanding information processing in neural systems (). Such models have played a central role in elucidating the mechanisms underlying synaptic transmission (), the action potential (), dendritic integration (), and, more recently, circuit function ().

These results show that ISN signature responses can occur in network models with large populations of excitatory and inhibitory cells interconnected with the extensive recurrent synaptic connectivity and conductance-based signaling mechanisms as found in cortical networks. Moreover, they establish that the ISN signature responses can be detected from somatic voltage-clamp recordings in layer 2/3 pyramidal cells, even when the synaptic input is integrated across the dendritic arbor. These results demonstrate that components of NeuroML models on OSB can be reused with the new tools and infrastructure to build complex multiscale circuit models.

To examine whether dendritic integration affects the ability to detect ISNs, we built a hybrid network model where some of the single-compartment excitatory cells were substituted with morphologically detailed layer 2/3 pyramidal cell models ( Figures S3 E and S4 A). Figure 7 D shows a screenshot of OSB with a 3D view of the hybrid network and a number of visualization panels showing the network activity, including raster plots, firing rate traces, and membrane potential plots for individual cells. As for the networks of point neurons, this hybrid network exhibited an increase in firing rate upon reduced excitatory drive to inhibitory cells, with a particularly strong effect observed in the morphologically detailed neurons. Voltage-clamp recordings from 2 of the 10 morphologically complex cells revealed a burst of excitatory and inhibitory synaptic currents during the period of reduced excitatory drive to a subset of inhibitory cells ( Figure 7 D, top and middle panels on right). Similar responses were observed when a hyperpolarizing current was applied to interneurons to mimic optogenetic inactivation by halorhodopsin ( Figures S4 B–S4D), consistent with recent voltage-clamp recordings from layer 2/3 cortical pyramidal neurons ().

To test whether ISN signature behavior is to be expected in real cortical circuits, we built three network models of increasing biological detail using NeuroML components present on OSB and the associated tools for model construction ( Figure 7 A; STAR Methods ). Reimplementation of the spiking network model fromin 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 7 B; cf. Figure 10B in), 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 inand 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 S3 A–S3D). A network model constructed with these cells and with synaptic connectivity fromalso exhibited the paradoxical effect of ISNs ( Figure 7 C).

(D) Similar network to (C), with 10 of the E cells replaced by detailed layer 2/3 cell model from; 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). A spike raster plot (bottom right) and corresponding firing rate traces (bottom left; average rate in 100-ms window per population) are also shown (population colors follow 3D network). The top and middle right-hand plots show currents from 10 independent network simulations when the somata of two morphologically complex cells were voltage clamped at −80 mV and 0 mV, revealing the excitatory and inhibitory postsynaptic currents, respectively.

(C) Network model in NeuroML consisting of point neurons with voltage-gated membrane conductances fromthat 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 S3 A–S3D.

(B) Model of ISN created in PyNN, exported to NeuroML, and visualized on OSB (top; 800 excitatory [E; red], 200 inhibitory [I; blue] cells; external spiking inputs, modeled as explicit populations in PyNN, shown in white). Spiking behavior of cells (middle) and population rate plots (bottom) during reduced excitatory synaptic excitation to 90% of the I cells (during shaded period input rate was reduced from 9,600 Hz to 9,200 Hz in these cells) are shown. Population rate plots (bottom; average firing rate of subpopulations smoothened with Gaussian kernel of width 30 ms) reveal firing mean rate increases despite lower input to these cells (dark blue; black dotted line is average of 40 simulations), as well as the rate of non-perturbed I (light blue) and E cells (red), confirming the presence of a signature ISN response under these conditions.

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 (). There is considerable interest in such inhibition-stabilized network (ISN) models (), as high-gain network regimes are thought to contribute to important functions, like signal amplification, noise tolerance, and pattern completion, and could underlie certain pathological states, such as epilepsy (). Moreover, recent experimental studies on the superficial layers of visual and auditory cortex support the idea that they operate as ISNs (). Networks operating in ISN regimes can be identified through their characteristic “signature,” which is a paradoxical inverse response of the inhibitory interneurons to alterations in excitatory drive (). This was predicted from highly simplified models where neuronal populations were modeled as single nodes and synaptic input was modeled as current. The simplicity of such models raises the question of whether more complex neuronal networks, composed of populations of excitatory and inhibitory neurons interconnected with more realistic recurrent synaptic connectivity and conductance-based signaling, behave in a similar manner (). Moreover, real cortical neurons receive synaptic input onto extensive dendritic trees, which can exhibit nonlinear behavior (). However, no previous model has explored whether ISN properties can be detected in neurons with realistic dendritic integration.

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.

We have developed a range of documentation and tools to facilitate the conversion of models into NeuroML and PyNN ( Figures 1 A and 1B). Figure S1 provides an overview of how these tools can be used at each stage of conversion of an existing model for use on OSB. A key advantage of the modular structure of NeuroML and PyNN is that model components can be automatically tested across multiple simulators using the OSB Model Validation (OMV) framework ( STAR Methods ). This allows automated tests to be run to check the expected behavior of models every time there is a change to the code in the repository and helps ensure the quality of the model components. Table S2 shows the range of simulator-specific tests on the OSB models discussed in this paper. To facilitate local execution and testing of models, we have created a self-contained software environment (a Docker image; STAR Methods Table S3 ) with all simulator tools preconfigured ( Table S4 ), as well as verified, stable releases of all models presented here. Using this tool, 351 individual tests across 23 simulator configurations in 27 projects can be run on any operating system supporting Docker ( Table S3 ). This demonstrates the broad coverage of model types and simulators that can benefit from automated testing and will help ensure OSB models and components are reproducible.

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 (), most existing models have been developed and defined in simulator-specific languages () 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 (). 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 (), NEST (), Brian (), and neuromorphic hardware (). 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 4 E–4H) and specific cell models in NeuroML can be used in PyNN scripts and run on supported simulators ( STAR Methods ).

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. To illustrate this functionality, we have extended a pre-existing tutorial on the Hodgkin Huxley model of the action potential for use on OSB ( Figure 5 A) and have created an interactive tutorial on modeling biophysically detailed, multicompartmental neurons using a layer 2/3 pyramidal cell from the Blue Brain Project ( Figure 5 B).

Simulations of neurons and circuits generate a large amount of data. Moreover, to examine behavior under different conditions, models must be run many times. To deal with these requirements, we have built a system for managing and storing simulation experiments on OSB. This enables registered users to run multiple simulations and to interactively explore the results ( Figure 5 ). The simulation results generated through OSB can also be downloaded in a zip file to the user’s computer or automatically uploaded to Dropbox for more detailed offline analysis ( STAR Methods ). In addition, the layout of the visualization panels showing the 3D morphology of the model and associated analysis panels can be saved between sessions. All changes to the model and its graphical visualization are recorded as a series of text-based instructions, ensuring that the simulation, analysis, and presentation are fully documented ( STAR Methods ). These can be accessed through a popup console tab ( Figure 5 A, bottom left), which allows a series of instructions to be copied, pasted, and rerun, as well as direct control of OSB through scripting.

Users can also alter values of model parameters through the browser, such as current injection levels and densities of ion conductances ( Figure 5 ). By running multiple simulations, this enables characteristic neuronal properties to be investigated ( Figure 6 A). More substantial changes to the model, such as adding new conductances or changing the number of cells, currently require offline regeneration of the NeuroML files ( Figure S1 ). Nevertheless, many changes can already be made to investigate cell and network behavior, such as setting a synaptic conductance to zero to remove the connection between two specific populations. The user also has control over the number of simulated variables recorded. For example, the membrane potential can be recorded from the soma or from all compartments in every cell. Recorded data can be replayed as variable-time plot or a pseudocolor representation can be used to indicate the voltage ( Figure 6 B) or calcium concentration ( Figure 6 C) across the morphology or across a population of neurons. The ability to analyze, visualize, and interact with models on OSB provides a unified online resource for accessing the structural and functional properties of complex models of brain function, thereby enabling greater scrutiny and insight into these powerful computational tools.

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 (), which provides parallel execution of models across hundreds of processors ( Figure 1 A; STAR Methods ). Upon completion, the data generated are sent back to the browser for visualization ( Figures 5 and 6 ). These features enable exploration of complex circuit models without the requirement for specialist knowledge to setup and run large-scale simulations ( Video S3 ).

(C) Left: small network of layer 2/3 pyramidal cells and interneurons from. Right hand plot shows time course of somatic calcium concentration in 7 cells during the simulation. Right bottom, scale for recorded calcium concentration as overlaid on morphologies is shown.

(B) Small network with 3 mitral and 15 granule cells from. Plots on right show somatic membrane potentials for 3 mitral (top) and 3 granule (bottom) cells. Panels below show current simulation time and color scale for the recorded membrane potentials, as displayed on the cell morphologies during simulation replay.

(A) Layer 2/3 pyramidal cell project from. Left: 3D cell morphology is shown. Top right: membrane potential recorded at 5 locations from a simulation of this cell receiving background synaptic stimulation is shown. Bottom right: membrane potential at soma for increasing levels of current injected at the soma is shown.

(B) Screenshot of interactive tutorial using a layer 2/3 pyramidal cell model () to illustrate how OSB represents biophysically detailed cells and how their functional properties can be explored. Right: interactive guide shows parameters that can be changed and suggestions for exploring behavior. Plots on left show membrane potential at multiple locations on cell (blue, soma; orange, end of axon; green and red, two dendritic locations) for 3 scenarios while 2-s current pulse is applied: original cell parameters (top); axonal sodium conductance removed (bottom left); and axial resistance reduced by factor of 10 (bottom right).

(A) Annotated screenshot of an OSB project for a single-compartment neuron with Hodgkin-Huxley type conductances (). Single-compartment model cell (yellow sphere), tutorial control panel for altering current, channel densities, and running simulations (right), list of previously run experiments with changed parameters (bottom), tab for enabling the interactive command line console (bottom left), and membrane potential plot showing spiking (orange) and subthreshold (blue) recordings (left) are shown.

The 3D structure of circuit models is often complex, as it can include multiple neuronal layers, a range of cell types distributed at different densities, and extensive synaptic connectivity. OSB facilitates visualization of network structure by automatically generating multiple types of connectivity diagrams. This is possible because NeuroML descriptions of such networks contain structured lists of 3D locations of somata and the subcellular location of chemical and electrical synapses. Figure 4 A shows a single-column thalamocortical model consisting of multicompartmental neurons distributed over multiple cortical layers (). The synaptic connectivity of such circuits can be inspected using automatically generated visualizations. A chord diagram ( Figure 4 B) provides a convenient way to assess the density or sparsity of the synaptic connectivity. In contrast, the connectivity matrix ( Figure 4 C) provides a more quantitative overview of the synaptic connections, showing the strength of excitatory and inhibitory connections between different cell populations. Lastly, the connectivity plot shown in Figure 4 D combines these features in one plot, providing a way to visualize the size of the neuronal populations, the connections between them, and their relative strength. This functionality enables the easy comparison of network connectivity. For example, a cortical network consisting of point neurons () can be analyzed and compared with the previous, more detailed cortical model ( Figures 4 E–4H). For large-scale networks with a high level of biological detail, such as the recently developed CA1 circuit model (), 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 4 I). However, this can be loaded in the background if required, enabling the properties of the synaptic connectivity to be visualized ( Figures 4 J–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 4 A–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.

(I–L) 3D view of network (I), chord diagram (J), adjacency matrix (K), and connectivity graph (L) for network model of hippocampal CA1 region fromwith 311 pyramidal cells (example cell in inset of I) and 24 interneurons of 8 different types. Network (0.1% of full scale) has over 1 × 10connections, primarily between pyramidal cells. All images are screenshots from browser visualization.

(E–H) 3D view of network (E), chord diagram (F), adjacency matrix (G), and connectivity graph (H) for point neuron spiking network model of, with 1,539 cells in 8 populations (E [excitatory] and I [inhibitory] from layer 2/3, layer 4, layer 5, and layer 6; 5.96 × 10connections; 2% of full-scale network).

(C) Adjacency matrix; lines on left and top indicate pre- and postsynaptic population colors, respectively. Squares represent relative strengths of excitatory (red) or inhibitory (blue) inputs (average weighted conductance of synaptic input from the presynaptic population to each postsynaptic cell).

(B) Chord diagram showing projections between populations (1 × 10 5 individual connections of 193 types). Outer ring color indicates population; the chords are attached to the presynaptic population outer ring segment, separated from the postsynaptic population ring segment and colored to match the postsynaptic population. Colors for populations match 3D view in (A).

To allow visualization of a model in the web browser, OSB searches for the standardized model descriptions (NeuroML files) in the repositories associated with the OSB project. This information is used to generate a 3D visual representation of the neuronal morphology and/or the circuit structure ( Figure 3 A). In addition, the spatial distribution of the density of ionic conductances can be viewed either in tabular form or as a pseudocolor density map superimposed on the neuronal morphology ( Figure 3 B). Because models of ionic conductances are also specified in NeuroML format, the underlying mathematical expressions defining the rates of activation and inactivation can be extracted and plotted ( Figure 3 C). Thus, the types, distributions, densities, and kinetic properties of the membrane conductances present in the model can be automatically exposed in graphical formats. Other useful information in the NeuroML files, such as authors of the files, references, and links to the original data sources, is also presented through the web interface. This facilitates transparency and enables the history of models and their authors to be recorded (provenance tracking).

(C) Cerebellar Golgi cell model from. Cell regions have been highlighted (blue soma, green dendrites, and orange axon). Left information panel for a low-voltage-activated Caconductance (Ca LVA) present on the cell is shown, including conductance expression and gating variables. Right plots show voltage dependences of time constant (top) and steady-state value (bottom) for the activation (orange, m) and inactivation (blue, h) gates. Dendrite and axon diameters are increased for clarity of figure presentation in (A)–(C).

(B) Layer 5 pyramidal cell from the Blue Brain Project neocortical microcircuit model. Bottom left: an information panel (opened via Model Description button) summarizing the types and densities of ionic conductances on the cell membrane is shown. Individual conductances can be clicked to highlight the regions of the cell where they are present. Cell morphology shows the non-uniform distribution of the hyperpolarization-activated conductance on the apical dendrite (low = yellow near soma; high = red in the distal dendrites).

(A) Screenshot showing 37 cell models of visual cortex neurons from the Allen Cell Types Database on the OSB website, visualized in 3D through a browser. Spiny (32 on left) and aspiny (5 on right) cells from layers 2/3 (red), 4 (blue), 5 (green), and 6 (orange) are shown.

Scientists wishing to use the infrastructure for model development, collaboration, testing, and dissemination form the core OSB user group ( Figures 1 A and 2 B). 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 example, there are deep links between OSB and ModelDB (), an archive of neuronal models in their original published formats. These features of OSB enable neuroscientists from many backgrounds to explore and use biologically detailed models and lower the technical barriers to the more advanced features of the platform.

OSB can also be used to develop online resources for teaching neuroscience to students and researchers. This is facilitated through interactive help functionality and a framework for building tutorials, which can be used to illustrate the biophysical, anatomical, and physiological properties of a model and to help explain different mechanisms, such as the conductances underlying the action potential and synaptic integration.

There are a number of different ways users can interact with models on OSB, depending on their goals and level of expertise in computational neuroscience and in programming ( Figure 2 B). Scientists interested in rapidly learning about the properties of a model used in a scientific study can readily inspect model structure and parameters and replay previously recorded simulations through their browsers without registering as an OSB user. The main OSB projects page ( http://www.opensourcebrain.org/projects ) provides links to a wide range of models, including all of those presented here. After registering and logging in, users can run and store their own simulations for a more in-depth analysis of the functional properties of the model.

OSB currently hosts standardized, curated models spanning a wide range of biophysical detail, varying from single cells up to large-scale networks with thousands of neurons ( Figure 2 A; Table S1 ). These models cover multiple regions of the brain, including neocortex (), cerebellum (), hippocampus (), and olfactory bulb (). A number of invertebrate models have also been converted (). At the single-cell level, there are models from the Allen Institute Cell Types Database () and the Blue Brain Project () and reconstructed neuronal morphologies from the NeuroMorpho.Org ) and Janelia MouseLight () 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.

(A) NeuroML- and PyNN-based models on OSB, identified by author(s) of the original publications describing the models. The models have different levels of biophysical detail, ranging from simple point neuron models (e.g., integrate and fire [I&F]) to complex multicompartment cell models. Some projects contain single cells, and others contain multiple cell types or network models. Neuronal and circuit models cover a broad range of brain regions and include both vertebrate and invertebrate systems. More details on these models are given in Tables S1 and S2 . See also Figure S2

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 1 A). These features were made possible by defining models in the neuroscience model description languages NeuroML () and PyNN (). 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 1 B). 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.

(B) Functionality following the conversion of published models from simulator-specific formats into standardized representations. This includes automated validation, visualization, analysis, and simulations on different platforms, using a variety of generic tools.

(A) Functionality of Open Source Brain (OSB) and interactions with users and external resources. (Left) Model sources for OSB are shown. Center: OSB resources to facilitate conversion of models in open source repositories to standardized formats; to validate against the standards; to test model code; and to visualize, analyze, and simulate models through a web browser are shown. A search function is provided, together with an application programming interface (API). Right: user interaction with projects can be through the OSB web interface or by command line. Wikis enable feedback, and issues can be opened. Project code can be cloned, forked, or committed using standard open source workflows or downloaded as zipped releases. Simulations can be performed on the OSB server or submitted to the Neuroscience Gateway for execution on their supercomputing facilities. See also Figure S1

Discussion

We have developed Open Source Brain, a web-based collaborative resource of standardized neuronal and circuit models together with tools and infrastructure for model development, testing, and reuse. The OSB platform enables web-browser-based visualization, analysis, and simulation of models without the need to install software or write code. This makes complex models accessible to the wider neuroscience community, enabling critical evaluation of model properties and behavior. The modular format used by OSB ensures that models and their components can more easily be reused for new scientific questions. By making neuronal and circuit models more accessible, transparent, and reliable, the OSB platform provides a powerful new resource for students, individual researchers, and collaborative research teams to learn about and investigate brain function in health and disease.

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.

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Lytton W.W. Evolutionary algorithm optimization of biological learning parameters in a biomimetic neuroprosthesis. The distinct functionality of OSB extends and complements that of ModelDB (), a well-established repository of models in computational neuroscience. ModelDB hosts model code in the original language in which it was developed and facilitates the reproduction of the results from their originating publications. OSB builds on this functionality by focusing on hosting standardized models that are independent of the simulator used, which enable users to interact with and analyze models in greater detail. Moreover, OSB is designed to reveal circuit-level properties, including connectivity and network dynamics ( Figures 4 and 7 ). More fundamentally, models hosted on OSB are not static as they are in ModelDB and can instead be developed, refined, and improved using the infrastructure for open source software development, automated validation, and testing of models. Deep links between these resources allow users to find the same model on either platform. Indeed, OSB actively encourages researchers to first submit their model to ModelDB following publication ( Figure S1 ). The step of moving a model onto OSB is an indication that one or more parties (who may not be the original developers) wish to standardize and further develop the model, making it more accessible to the wider neuroscience community and extending it for use beyond the original publication. OSB also supports sharing of model code prior to publication, e.g., the ongoing development of a large-scale network model of primary motor cortex ().

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Wehr M. Rapid rebalancing of excitation and inhibition by cortical circuitry. As our development of biologically detailed cortical network models illustrates, new models can be built from the existing standardized components present on OSB by optimizing them against available data ( Figure 7 ). By using model elements from different projects to build ISN network models with different degrees of biological detail, we show that the ISN signature responses that were predicted from highly simplified models () are also expected in biologically detailed models that include conductance-based spiking mechanisms and can be detected in cells with complex dendritic morphologies. Predictions from these models can be used to refine experimental approaches for detecting ISNs (), to explain why they might not be detectable under some experimental conditions, and to investigate how dendritic properties interact with the nonlinear dynamics of ISNs at the network level.