Dynamical models provide an essential counterpart to experiments in the endeavor to understand the brain, and today a large ecosystem of model types and approaches exists (Figure 1). For example, hypothesis-driven modeling typically brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the brain works. Data-driven modeling, on the other hand, often follows a more unbiased approach, with model construction informed by the computationally intensive use of data. Although hypothesis- and data-driven modeling approaches are not mutually exclusive, a plethora of modeling formalisms, simulation platforms, and data formats has fragmented the neuroscience modeling community, particularly when it comes to modeling at different biological scales or levels of abstraction. This diversity is beneficial: most models and model building tools have a clear and specific role, but at the same time combining these approaches in an interoperable way would have an immense impact on our understanding of the brain. The FAIR (Findable, Accessible, Interoperable, Reusable) principles (Wilkinson et al., 2016) have been widely discussed and implemented for data management (Wittig et al., 2017), and recently also for computational workflows (Goble et al., 2020). We suggest that these principles would be beneficial for models and modeling workflows within neuroscience as well. In this review, we examine the different steps of a general modeling workflow and look at different alternatives for model building, refinement, analysis, and use (Figure 2). For each step we consider how the FAIR principles can be applied to the different aspects of the modeling process. We focus on models at the intracellular or cellular level, where the field of computational neuroscience meets systems biology modeling, with an emphasis on models of brain plasticity and learning. However, the discussed methodology and concepts can be applied to other biological systems and scales as well (see, e.g., Einevoll et al., 2019).

Figure 1

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Figure 2

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As an illustration of the wide variety of model types, Figure 1 depicts a number of models arranged according to four criteria: biological scale, level of abstraction, and to what degree hypothesis- and data-driven approaches were used during model development. The axes of the figure are qualitative, additional dimensions could be considered, and the precise arrangement of the models could be interpreted differently as there are no strict metric spaces defined. Regarding biological scale, we note that experimental data can be measured and applied at different resolutions — from a molecular signaling pathway in a single synapse to an entire brain. This is reflected in the models, which exist, for example, from the level of molecular interactions important for plasticity in specific synapses (Bruce et al., 2019b) to brain structures with an aim of understanding generalized learning rules (Gurney et al., 2015). Because of different modeling goals, models are also constructed with different levels of abstraction. For example, the dopamine signal is important for reward learning, addiction, and motivation, and has been modeled phenomenologically with a reward-prediction error (Frémaux et al., 2013) or more mechanistically, taking into account dopamine diffusion and the affinity of dopamine receptors (Dreyer et al., 2010). Figure 1 also includes the data- and hypothesis-driven aspects of models. Models that have a high score on the data axis are typically based on a large amount of quantitative data and/or have been developed using data science methodology. Models that have a high score on the hypothesis axis, on the other hand, are strongly driven by a particular question or idea, and one aim could be to investigate if some assumed mechanism explains the studied phenomena. As a side note, we have used the concepts of ‘top-down’ and ‘bottom-up’ modeling restrictively within this article since those concepts are often implicitly linked to different field-dependent contexts, especially concerning the intensive use of data in systems biology and neuroscience, for example compare (Bruggeman and Westerhoff, 2007) and (Eliasmith and Trujillo, 2014; Dudai and Evers, 2014).

All these different types of models can be useful, but their value is greatly increased if they can be integrated to understand the same experimental data and phenomena at multiple scales or different levels of abstraction. For example, an abstract model of reward prediction can be given details through multicompartmental modeling, allowing this new form to be directly compared to experimental data. An experimentally validated model can then be used for designing new experiments or to predict efficient therapeutic interventions. Across all model types, it is also important to support new tools for model validation and refinement including methods for efficient parameter estimation, global sensitivity analysis, uncertainty quantification, and other data science approaches (Alber et al., 2019). Not only is this critical for increasing the accuracy and reproducibility of model development, but also for the efficient inclusion of new data. A FAIR modeling infrastructure that includes both models, data and software is essential for establishing synergy among modeling approaches and for the refinement and validation of models.

Wilkinson et al., 2016 subdivided the FAIR principles into 15 subcategories that describe important details regarding scientific data that are Findable, Accessible, Interoperable, and Reusable. The intent was to not only consider ‘data’ in the conventional sense but also the algorithms, tools, and workflows that led to the data. In this review, we follow this path and consider FAIR with regard to models and modeling tools in addition to experimental data. While all 15 subcategories should be considered by modeling software and database developers, here we focus on the subset of actions that can be taken by individual researchers, and we avoid technological complexity in our suggestions. The principles of findability and accessibility for models and modeling workflows both require the availability of databases and repositories where models, software and associated data can be stored, and the use of persistent, unique identifiers and metadata. When it comes to interoperability of models, our main focus concerns the ability to run the same model using different simulation platforms and model analysis tools. Nonetheless, we also discuss interoperability across neighboring scales, such as when the output from a model at a finer resolution is used as the input to a model at a more coarse-grained resolution. Concerning reusability, our main focus is that different laboratories should be able to reuse and rebuild each other’s models efficiently, while acknowledging the provenance of a model, as well as the data used to constrain and validate the model.

Reusability is related to the issue of research reproducibility, which has been discussed within many scientific fields. In a recent study (Tiwari et al., 2021), the authors attempted to reproduce more than 400 kinetic models of biological processes published in peer-reviewed research articles in conjunction with the curation process in the BioModels (Chelliah et al., 2015) repository, and they found that only about half of the models could be directly reproduced. They note that there is a difference between reproducibility and repeatability. In principle, a modeling process is repeatable if someone else can run the same code and get the same results. Reproducibility on the other hand requires that a different researcher starting from the same information but with another implementation reaches the same result. Reproducibility therefore provides a stronger quality check for computational scientific results. Other interpretations of these terms exist, as discussed in a recent study (Plesser, 2017), but here we follow the terminology described above.

In order to further develop modeling workflow capabilities fulfilling the FAIR criteria, a tightly integrated ecosystem of databases, software, and standardized formats is needed. Many of these pieces exist, supporting the modeling process from initial model development, to the use of models in exploration and prediction. For example, over the years, the field of computational neuroscience has developed a set of open-source simulation environments (Gewaltig and Cannon, 2014). At the same time, the community is depositing published models into online repositories, such as model databases (Birgiolas et al., 2015; Gleeson et al., 2019; Hines et al., 2004; Li et al., 2010) and GitHub (github.com), for others to inspect or use. Simulator independent, standardized model specifications such as the Systems Biology Markup Language (SBML) (Hucka et al., 2003) and NeuroML (Gleeson et al., 2010) have been developed to promote model interoperability and reproducibility. Finally, increasingly, experimental data are being used extensively in the modeling process, and frameworks for model calibration (Ashyraliyev et al., 2009; Mitra and Hlavacek, 2019; Sun et al., 2012) and for validating models against data (Omar et al., 2014) are available, or under development. What remains is to develop standardized workflows and data formats that integrate the components of this ecosystem to facilitate reusing, extending, and comparing models, within and across scales. The aim is to promote interoperability among different software packages used within neuroscience, to increase shareability of models, and at the same time improve transparency and reproducibility with regard to the model building process and the data used for parameter calibration and validation. All this can in the end also contribute to increased automation of the entire process to set the stage for wider use of data intensive methods.

As we consider the modeling workflow shown in Figure 2, we discuss how FAIR principles can be introduced to this process. To illustrate these steps with a concrete example, we use a classical model of activity-dependent synaptic plasticity, the Bienenstock–Cooper–Munro (BCM) rule (Bienenstock et al., 1982), described in detail below. Here, plasticity depends on pre- and postsynaptic activity, and leads to long-term depression (LTD) or long-term potentiation (LTP). The BCM rule is chosen as an example because it was first modeled phenomenologically and also has been subsequently reproduced using mechanistic models with increasing levels of detail as more data and knowledge have accumulated (Lisman, 1989). More recent models of signaling underlying the BCM rule are substantially data driven (Castellani et al., 2005; Hayer and Bhalla, 2005).

An example: the BCM rule

The BCM rule is a synaptic plasticity model (Bienenstock et al., 1982) formulated in the context of the development of orientation selectivity in the visual system. It states that as postsynaptic activity increases, there are two domains of plasticity that induce either depression or potentiation. This rule has since been used as the basis for considerable theoretical and experimental work on synaptic plasticity, and here it is employed to illustrate approaches for modeling plasticity and to motivate our discussion of modeling workflows and FAIR approaches.

The original BCM rule describes the rate of change of synaptic weight as

$\frac{dm\left(t\right)}{dt}=\varphi \left(c\left(t\right)\right)s\left(t\right)-ϵm\left(t\right),$

where is the synapse input current, $ϵ$ is the time constant of synaptic decay, and ${\displaystyle c}$ is the postsynaptic activity. ${\displaystyle \varphi }$ represents the postsynaptic activation function, formulated as

$\varphi (c)<0\mathrm{f}\mathrm{o}\mathrm{r}c<{\mathrm{\Theta }}_{\mathrm{M}}\mathrm{a}\mathrm{n}\mathrm{d}\varphi (c)>0\mathrm{f}\mathrm{o}\mathrm{r}c>{\mathrm{\Theta }}_{\mathrm{M}}$

where ${\displaystyle {\mathrm{\Theta }}_{\mathrm{M}}}$ is the activity threshold at which the synaptic strengths are modified. The BCM rule is phenomenological because ${\displaystyle \varphi }$ does not map to any biological mechanisms, and the authors showed that this learning rule can account for the formation of orientation selectivity for a wide range of values for ${\displaystyle {\mathrm{\Theta }}_{\mathrm{M}}}$_. A typical graph of ${\displaystyle \varphi }$ is depicted in Figure 3A, and similar curves have been obtained experimentally (Kirkwood et al., 1996).

Figure 3

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This model was published in a widely read society journal and predates today’s FAIR principles. The article included simulations of the model equations, but, lacking today’s open-source, code-sharing infrastructure, readers were left to their own skills to replicate these implementations. Like many abstract models, the BCM model is a mostly qualitative representation of concepts derived from a substantial amount of data, including many features of synaptic plasticity, network-level data for orientation selectivity, and data for the degradation of orientation selectivity in the case of monocular deprivation and similar experiments. The publication includes simulations of several such cases, showing a qualitative match to data but does not explain how parameters were derived. In summary, due to the minimal, abstract nature of the model, the BCM study is not difficult to reproduce; however, there is no reference implementation available for simulations, and the parameters for several simulations are not available. Thus, the published simulations and parameters are not consistent with today’s FAIR principles.

Moving forward a few decades, one finds mechanistic, mass-action models of synaptic signaling and the BCM rule, where the relevance of FAIR and data-driven approaches becomes more apparent. First, the quantities under consideration (input activity and synaptic strength) correspond directly to synaptic molecules. Experimental data tell us that intracellular calcium concentration is a good proxy for postsynaptic activity, ${\displaystyle c}$, and that the phosphorylation state of the AMPA receptor is one of the proxies for synaptic efficacy, ${\displaystyle m}$. Filling in the reaction diagram, one obtains a simplified reaction network of the form shown in Figure 3B. Remarkably, the properties of these molecules result in a response that resembles the BCM curve. The phosphatase calcineurin (CaN) has a high affinity for calcium bound calmodulin, and also can partly become activated directly by calcium (Creamer, 2020). Hence, it is activated at moderate calcium and triggers an initial decrease in phosphorylated AMPAR and LTD (${\displaystyle \varphi (c)<0}$ from the BCM formulation). Calcium/calmodulin-dependent protein kinase II (CaMKII) has a weaker affinity for calcium bound calmodulin, but is present in overwhelming amounts, so its activation surpasses CaN at high calcium concentrations, thus implementing ${\displaystyle \varphi (c)>0}$ for ${\displaystyle c>{\mathrm{\Theta }}_{\mathrm{M}}}$, and leading to increase in phosphorylated AMPAR and thus LTP. The resulting curve from this mechanistic but simplified model matches the BCM curve (Figure 3C). More detailed mechanistic versions of the BCM model have been implemented using known synaptic chemistry in considerably greater detail (Lisman, 1989; Hayer and Bhalla, 2005).

How FAIR and data driven are these more recent models? The Lisman, 2017 is not consistent with our current definition of FAIR: it provides full disclosure of equations and rate constants, but reuse requires reimplementation. The more recent model (Hayer and Bhalla, 2005) exists in at least two open-access databases, the Database of Quantitative Cellular Signaling, DOQCS (Sivakumaran et al., 2003), and BioModels (Li et al., 2010), hence is findable and accessible. It has been converted to the standardized SBML model description format, promoting interoperability and reusability. The DOQCS version has citations and calculations for the derivation of some of the parameters, and the BioModels version maintains the provenance, referencing the DOQCS entry from which it was derived.

Parameter access greatly improves as models become FAIR. For example, Lisman’s study acknowledges the original BCM model as a key motivation (Lisman, 1989), but no parameters were available, leading Lisman to use ‘plausible’ estimates for rates that form the BCM curve. In contrast, Hayer and Bhalla had access to reaction schemes and rates from databases such as DOQCS (Sivakumaran et al., 2003), and model construction was facilitated considerably by the FAIR principles. However, the database version of this model does not include the provenance for the data underlying some of the parameters, in part because data were unavailable. Building on Hayer and Bhalla, 2005, numerous studies benefited from increasing convergence toward FAIR principles and have derived model structures and parameters from models placed in accessible repositories (e.g., Li et al., 2012; Nakano et al., 2010; Mäki-Marttunen et al., 2020).

To summarize, the history of models that implement the BCM rule demonstrates a clear progression from abstract, qualitative, and unFAIR formulations to more mechanistic, quantitative, and FAIR formulations of the same conceptual model using data-driven parameterization. This progression is a natural consequence of the accumulation of experimental data and nascent concepts of FAIR principles. In what follows, we consider how to improve data-driven models further through FAIR workflows and data science-based approaches.

A typical model development process can be divided into different stages or modules, as illustrated in Figure 2. The model foundation (Figure 2, box 1) stage corresponds to the process of collecting the experimental data and prior knowledge that will be used for the rest of the modeling process and combine these into an initial plan for the model structure (or topology), that is, a description of the different model entities and how they interact. In the next stage, the specification of the model and data (Figure 2, box 2a), the information from step 1 is formalized into a standardized, machine readable format. During the model refinement stage (Figure 2, box 2b), the model is transferred to a mathematical formalism, simulated, calibrated, and refined. After model refinement is complete, the specification files are to be updated with this new information. Finally, during the model usage stage (Figure 2, box 3), the resulting model is validated, analyzed, and used to predict or investigate additional phenomena. Some examples of tools and formats that can be used in this process are given in Table 1–4. Note that these tables do not provide an exhaustive list, but rather a starting point for discussion.

Model validation and usage

Following the model refinement process, we obtain the final version of the model that can be validated, analyzed, and used in simulation studies to make predictions and test hypotheses (Figure 2, Box 3). In multiscale modeling the output from the model can also be used as the input for another scale. Ideally, before the model is used for predictions, it is validated against data that were not used in the model construction and refinement process. However, data may be sparse requiring that validation occur through experimental tests of model predictions. After the model is finalized, the predictions from the model can be extended and better informed through model analysis. This could be done by sensitivity analysis, with tools from dynamical systems theory or through experimental design. Some examples of tools for model analysis are listed in Table 4.

Sensitivity analysis is a methodology that investigates how different input factors, such as model parameters or model input, affect the model output (Saltelli et al., 2007; Zi, 2011).The output could be the concentration of the active form of a protein as an example. Sensitivity analysis can be performed through local methods, which considers the partial derivatives in the close neighborhood of a point in parameter space, or global methods, which investigate a much larger region of the parameter space, often with statistical methods. Local methods are computationally efficient, but can be problematic due to limited range. Global sensitivity analysis on the other hand investigates a larger region, but is computationally costly. One example where a global sensitivity analysis was used is the study by Halnes et al., 2009. There are, however, also coarse-grained global sensitivity analysis methods as well as novel methods that interface coarse-grained global with local sensitivity analyses methods. Several studies have also investigated the sensitivity by a so-called one-at-a-time approach, where parameters are perturbed one at a time, for example Gutierrez-Arenas et al., 2014. Finally, a Bayesian approach for uncertainty quantification can also be combined with global sensitivity analysis (Eriksson et al., 2019), as illustrated in Figure 4. Tennøe et al. have recently developed a software for global sensitivity analysis (Tennøe et al., 2018).

Figure 4

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In an experimental design process, models can be used to simulate a large number of different scenarios corresponding to different potential experimental setups and in this way be used to design experiments that provide the most amount of new information (Kreutz and Timmer, 2009). Statistical methods have been developed for this, often using Bayesian methodology, see for example Liepe et al., 2013.

Another way of analyzing dynamical models is to use tools from dynamical systems theory. Such methods typically examine asymptotic stability, provide phase diagrams, and identify bifurcations in parameter space that identify and explain how major changes in model behavior rely on parameters or input. This approach has, as an example, been used to better understand systems level mechanisms (e.g., how a cell goes from silent to spiking, or how oscillatory phenomena in networks might be controlled [Ermentrout et al., 2019; Keeley et al., 2017]). With regard to subcellular-level phenomena important for plasticity, intracellular calcium release (Li and Rinzel, 1994) as well as switch-like behaviors have been investigated, for example CaMKII and protein kinase M – zeta (PKMζ) (Graupner and Brunel, 2007; Helfer and Shultz, 2018; Pi and Lisman, 2008). For instance, stability analysis shows that CaMKII can be at two stable states at resting intracellular calcium concentration (Graupner and Brunel, 2007), and the system of coupled CaMKII and protein phosphatase A (PP2A) could behave as a tristable system that describes LTP and LTD (Pi and Lisman, 2008). The original BCM study also included analysis with tools from dynamical systems theory to test whether the plasticity rule was stable.

There are many options for assembling the resources and approaches described to create specific modeling workflows that follow the steps outlined in Figure 2. A flexible workflow going through all the steps requires:

1. Much effort toward identifying the scientific questions to be addressed and the model prerequisites (see ‘Model foundation’).

2. A well-defined format for the model, the quantitative experimental data, and the assumptions on prior parameter ranges or distributions (see ‘Specifications of model and data’).

3. A simulator that supports a machine readable, standardized specification of the model (see ‘Mathematical formulation and simulation’).

4. A tool for searching the parameter space, that compares the simulations with the experimental data guided by the parameter prior information (see ‘Model refinement’).

5. Tools for model analysis (see ‘Model usage and validation’).

6. Parsing tools to go between the specification of model and data format and internal formats for the model refinement and model analysis tools.

In Figure 5, we provide three specific example workflows for subcellular- and cellular-level modeling that traverse these steps in different ways. These examples illustrate recent steps toward FAIR workflows and provide a starting point for further discussion within the community.

Figure 5

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Aside from single steps and components, we will now consider some questions that are important for all parts of the workflow, and in relation to this we also recommend the article by Goble et al., 2020, which describes FAIR computational workflows from a more general perspective. The first question concerns findability and here we emphasize the importance of metadata and providing unique and stable identifiers to the workflow components. Concerning models and data, some databases provide a unique Digital Object Identifier (DOI) for its constituents. This can be retrieved when the model or data are deposited in the database and included in the metadata of the workflow. Each resource and software used in the workflow should also be indicated using the associated, unique SciCrunch Research Resource Identifier (RRID) (Vasilevsky et al., 2013; Bandrowski and Martone, 2016), if it exists. It can be noted that in order to get an identifier to a more complex research object a general database like Zenodo (European Organization For Nuclear Research, 2013) could be used. As an example it is possible to use Zenodo to get a DOI for a GitHub repository release.

The second question concerns approaches for the packaging, description, and dissemination of the entire model building workflow, which is important for transparency and reproducibility. FAIRDOMhub (Wolstencroft et al., 2017) is a repository and collaboration environment for publishing FAIR data, operating procedures, and models for the systems biology community. EBRAINS has a similar ambition to provide a collaboration hub for brain research. Virtual machines or containers (Merkel, 2014) can play a role in reproducing all steps and results of a workflow. Easily accessible technologies such as Jupyter notebooks (Kluyver et al., 2016) can aid in interoperability, dissemination, and collaboration. The Common Workflow Language allows users to fully specify inputs, outputs, and other execution details needed for tools in a workflow and instructions for executing them within containers (Piccolo et al., 2021). The workflow around a model can also be made public analogous to publishing the protocol of a lab experiment, for example via protocols.io (Teytelman and Stoliartchouk, 2015). The recently published Research Object Crate (RO-Crate) provides a way to package and aggregate research ‘artifacts’ with their metadata and relationships (Soiland-Reyes et al., 2022), like spreadsheets, code, examples, and figures.

Finally, we illustrate a FAIR workflow by considering how one would today develop simulations for the BCM curve in Figure 3. Even though the original BCM model and many subsequent experimental studies predate the advent of FAIR principles, it is interesting to think about what would need to be done in order to make them FAIR compliant. Ideally, these studies would be Findable through PubMed and other resources. This implies that both metadata and data are easy to locate for both humans and computer programs. We would probably rely more heavily on later models, for example, Hayer and Bhalla, 2005, that are easily Accessible and in an Interoperable format such as SBML or SBtab. The Hayer model is available via the DOQCS database (Sivakumaran et al., 2003). In order to be Reusable, models should be replicable and compatible in different contexts. Quantitative experimental data could today be specified using FindSim or SBtab formats. Both formats support the comparison of model behavior with the experimental data, which is necessary for model refinement. For the model refinement to be reproducible it is important that the prior assumptions on parameters are described. It should be noted that the provenance for the Hayer model is not machine readable, though ideally it would be. Furthermore, more comprehensive metadata and appropriate license information should be included. Finally, since the refined model is defined in SBML, it could be used on any of several dozen simulator platforms (Table 3), and uploaded back to the Accessible databases to become part of the FAIR ecosystem.

We have investigated how data- and hypothesis-driven modeling approaches can be combined, through a FAIR infrastructure, in order to improve modeling capabilities within the neuroscience community. We suggest a minimal format for the ‘specification of model and data’ (Figure 2, box 2a) in order for the modeling process to be reproducible. We also describe different databases, formats, and software (Tables 1–4) and methods for combining them to achieve FAIR modeling workflows. We believe that such workflows would increase the capacity for combining different types of models, extending models as new data accumulates, and validating existing models.

When it comes to model refinement (Figure 2, box 2b), we have emphasized the importance of uncertainty quantification with for example Bayesian methods (Figure 4) in order to describe the uncertainty in the parameter estimates and model predictions. However, currently such methods are only possible (in practice) for models of the order of some dozens of parameters, pointing to a need for further method development. Concerning approaches for model analysis (Figure 2, boxes 2b and 3) an important aspect is to characterize models more objectively, thus avoiding the situation that the model has been ‘tuned’ to give a certain outcome. This is especially important as the goal of a model often is to reproduce specific observed behaviors in order to validate a hypothesis, rather than being predictive. This is particularly true for abstract, phenomenological models; however, data-driven mechanistic models that are portrayed as predictive often play the same, useful postdictive role. Many studies of such models now conduct parameter sensitivity analysis and parameter explorations to discover regimes in which new properties emerge, thereby providing an unbiased overview of the possible properties of the model.

In the future, it is likely that machine learning tools increasingly will be integrated with traditional modeling approaches as a complement but also a necessity for dealing with high throughput data of varying fidelity (Alber et al., 2019). While machine learning approaches are useful for finding correlations in big data, traditional mechanistic models are important for revealing causal relationships and mechanistic explanations. Mechanistic models also have the added possibility to generalize to new situations not considered during the model construction process.

If we want to understand the brain — from molecules to behavior — we need to model the brain at all biological scales to integrate and interpret various heterogeneous experimental data across scales. Here we have examined the opportunities associated with models arising at the scale where subcellular signaling processes meet the cellular level, corresponding to the field where the systems biology community meets the computational neuroscience community. Although the development of resources that integrate the FAIR principles into the computational neuroscience field has progressed over the last decade (e.g., compare De Schutter, 2008), standards and best practices still need to be further developed and aligned, an important endeavor coordinated by INCF (Abrams et al., 2021). The adoption of FAIR principles is timely due to the recent debut of brain initiatives around the world (Grillner et al., 2016), including the establishment of the International Brain Initiative (International Brain Initiative, 2020). Such efforts will help promote data and model exchange and reuse through international research and infrastructure collaborations.

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Author details

1. Olivia Eriksson

   Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Writing – original draft, Writing – review and editing

   For correspondence

   olivia@kth.se

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-0740-4318

2. Upinder Singh Bhalla

   National Center for Biological Sciences, Tata Institute of Fundamental Research, Bangalore, India

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   Reviewing Editor, eLife

   "This ORCID iD identifies the author of this article:" 0000-0003-1722-5188

3. Kim T Blackwell

   Department of Bioengineering, Volgenau School of Engineering, George Mason University, Fairfax, United States

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-4711-2344

4. Sharon M Crook

   School of Mathematical and Statistical Sciences, Arizona State University, Tempe, United States

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-1659-0749

5. Daniel Keller

   Blue Brain Project, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3280-6255

6. Andrei Kramer

   1. Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
   2. Department of Neuroscience, Karolinska Institute, Stockholm, Sweden

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-3828-6978

7. Marja-Leena Linne

   Faculty of Medicine and Health Technology, Tampere University, Tampere, Finland

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2577-7329

8. Ausra Saudargienė

   1. Neuroscience Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania
   2. Department of Informatics, Vytautas Magnus University, Kaunas, Lithuania

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-2768-3334

9. Rebecca C Wade

   1. Molecular and Cellular Modeling Group, Heidelberg Institute for Theoretical Studies (HITS), Heidelberg, Germany
   2. Center for Molecular Biology (ZMBH), ZMBH-DKFZ Alliance, University of Heidelberg, Heidelberg, Germany
   3. Interdisciplinary Center for Scientific Computing (IWR), Heidelberg University, Heidelberg, Germany

   Contribution

   Conceptualization, Funding acquisition, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5951-8670

10. Jeanette Hellgren Kotaleski

    1. Science for Life Laboratory, School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Stockholm, Sweden
    2. Department of Neuroscience, Karolinska Institute, Stockholm, Sweden

    Contribution

    Conceptualization, Funding acquisition, Investigation, Methodology, Project administration, Writing – original draft, Writing – review and editing

    For correspondence

    jeanette@kth.se

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-0550-0739

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