Combining hypothesis- and data-driven neuroscience modeling in FAIR workflows

Abstract

Modeling in neuroscience occurs at the intersection of different points of view and approaches. Typically, hypothesis-driven modeling brings a question into focus so that a model is constructed to investigate a specific hypothesis about how the system works or why certain phenomena are observed. Data-driven modeling, on the other hand, follows a more unbiased approach, with model construction informed by the computationally intensive use of data. At the same time, researchers employ models at different biological scales and at different levels of abstraction. Combining these models while validating them against experimental data increases understanding of the multiscale brain. However, a lack of interoperability, transparency, and reusability of both models and the workflows used to construct them creates barriers for the integration of models representing different biological scales and built using different modeling philosophies. We argue that the same imperatives that drive resources and policy for data – such as the FAIR (Findable, Accessible, Interoperable, Reusable) principles – also support the integration of different modeling approaches. The FAIR principles require that data be shared in formats that are Findable, Accessible, Interoperable, and Reusable. Applying these principles to models and modeling workflows, as well as the data used to constrain and validate them, would allow researchers to find, reuse, question, validate, and extend published models, regardless of whether they are implemented phenomenologically or mechanistically, as a few equations or as a multiscale, hierarchical system. To illustrate these ideas, we use a classical synaptic plasticity model, the Bienenstock–Cooper–Munro rule, as an example due to its long history, different levels of abstraction, and implementation at many scales.

Reduced oriens-lacunosum/moleculare (OLM) cell model identifies biophysical current balances forin vivotheta frequency spiking resonance

“…In closing, we would like to heartily agree with statements that “diversity is beneficial” to have an “immense impact on our understanding of the brain”, as stated in an excellent, recent review on neural modelling that pushes for combinations and not fragmentation (Eriksson et al, 2022). Our work has used developed biophysically detailed mathematical models based on in vitro data, created artificial in vivo states with reduced biophysical models to capture the range of firing frequencies in vivo , and directly extracted parameter values from voltage recordings of an experimental proxy (i.e., detailed, multi-compartment models).…”

Reduced oriens-lacunosum/moleculare cell model identifies biophysical current balances for in vivo theta frequency spiking resonance

“…In closing, we would like to heartily agree with statements that "diversity is beneficial" to have an "immense impact on our understanding of the brain, " as stated in an excellent, recent review on neural modeling that pushes for combinations and not fragmentation (Eriksson et al, 2022). Our work has used developed biophysically detailed mathematical models based on in vitro data, created artificial in vivo states with reduced biophysical models to capture the range of firing frequencies in vivo, and directly extracted parameter values from voltage recordings of an experimental proxy (i.e., detailed, multi-compartment models).…”

Hyperpolarization-activated cation channels shape the spiking frequency preference of human cortical layer 5 pyramidal neurons

“…The detail needed in any model naturally varies with the question, and there is no clear consensus of the extent of experimental detail needed as we consider the multiscale nature of the brain (D' Angelo and Jirsa, 2022). Given these challenges, clarity regarding model generation and the goals of computational studies are required to accelerate our understanding of the brain (Eriksson et al, 2022) and usher interdisciplinary neuroscientists towards jointly tackling the challenges of neurodegenerative disease at cell and circuit levels (Farrell et al, 2019;Gallo et al, 2020;Rich et al, 2022). The normalized plots highlight the "flatter" decay of the FDG at high frequencies when the Ih maximum conductance is halved and the more precipitous drop when the Ih maximum conductance is doubled.…”