An in silico framework for dissecting the mechanistic origins of in vivo recorded neuronal activity.

Overview

1. Max Planck Institute for Neurobiology of Behavior - caesar, Bonn, Germany
2. International Max Planck Research School (IMPRS) for Brain and Behavior, Bonn, Germany
3. Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands

Abstract

How can we identify the mechanistic origins of the electrophysiological activity that is recorded from neurons in the living brain? A promising strategy for addressing this question is to generate biologically realistic models of in vivo recorded neurons, and simulate how they transform synaptic inputs from the network into their observed neuronal activity. For this purpose, we here provide our approaches for the generation, simulation, and analysis of network-embedded neuron models as an open source, fully documented and freely available software environment: In Silico Framework (ISF). ISF is centered around the concept of achieving “model consensus” about the mechanistic origins of in vivo recorded activity across biologically diverse sets of models. To achieve such model consensus, ISF offers three key workflows. First, ISF enables users to generate models that are equally well constrained by empirical data at subcellular, cellular and network scales, while the set of models as a whole is constructed to exhibit maximally diverse parameters, spanning the full ranges permitted by the empirically observed biological variability at each scale. Second, ISF enables users to identify those subsets of model configurations that predict the in vivo observations without being tuned to do so. Third, for each of those model configurations, ISF enables users to identify which mechanisms at subcellular, cellular and network scales are necessary to predict the in vivo observations, and which mechanisms are dispensable. Thereby, ISF can reveal which mechanisms are common across model configurations, and whether the diversity of model configurations could account for the variability of the in vivo observed activity across animals, cells and trials. In essence, by achieving such model consensus, ISF predicts mechanisms that are robust across biological variability, and which may hence indeed be used in vivo. Finally, ISF enables users to derive model consensus for in silico manipulations, to identify which experimental strategies would be best suited to test the predicted mechanisms in vivo. We exemplify how we have used this iterative in silico - in vivo approach of ISF to dissect the mechanistic origins of sensory responses in the barrel cortex. By making ISF available as a standalone online resource, we believe it will facilitate the generation, simulation and analysis of models that reveal mechanistic origins of in vivo recorded activity beyond the barrel cortex for which it was originally designed.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

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Data Availability

All code and data presented in this paper are available without restrictions from https://mpinb.github.io/in_silico_framework/index.html.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 9 MeSH terms, 9 funders, 58 references.

Cite

This paper

Meulemeester, B., Bast, A., Royo, M., Fruengel, R., Saka, S., Kastrinakis, F., & Oberlaender, M. (2026). An in silico framework for dissecting the mechanistic origins of in vivo recorded neuronal activity. PLoS computational biology, 22(8), e1014617. https://doi.org/10.1371/journal.pcbi.1014617

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