A universal workflow for creation, validation, and generalization of detailed neuronal models

Reva, Maria; Rössert, Christian; Arnaudon, Alexis; Damart, Tanguy; Mandge, Darshan; Tuncel, Anıl; Ramaswamy, Srikanth; Markram, Henry; Van Geit, Werner

doi:10.1016/j.patter.2023.100855

Cited by 3 publications

(1 citation statement)

References 62 publications

(101 reference statements)

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“…The population-of-models approach is now an established route to systematically study heterogeneities and differential dependencies of functional outcomes in several biological neuronal subtypes (Foster et al, 1993;Goldman et al, 2001;Prinz et al, 2003;Marder and Taylor, 2011;Rathour and Narayanan, 2012;Basak and Narayanan, 2018;Migliore et al, 2018;Mittal and Narayanan, 2018;Rathour and Narayanan, 2019;Basak and Narayanan, 2020;Goaillard and Marder, 2021;Roy and Narayanan, 2021;Mittal and Narayanan, 2022;Arnaudon et al, 2023;Nagaraj and Narayanan, 2023;Reva et al, 2023;Roy and Narayanan, 2023), including DG granule cells (Beining et al, 2017b;Mishra and Narayanan, 2019;2021c;Schneider et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Ion-channel degeneracy and heterogeneities in the emergence of signature physiological characteristics of dentate gyrus granule cells

Kumari,

Narayanan

2024

Preprint

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Complex systems are neither fully determined nor completely random. Biological complex systems, including single neurons, manifest intermediate regimes of randomness that recruit integration of specific combinations of functionally segregated subsystems. Such emergence of biological function provides the substrate for the expression of degeneracy, the ability of disparate combinations of subsystems to yield similar function. Here, we present evidence for the expression of degeneracy in morphologically realistic models of dentate gyrus granule cells (GC) through functional integration of disparate ion-channel combinations. We performed a 45-parameter randomized search spanning 16 active and passive ion channels, each biophysically constrained by their gating kinetics and localization profiles, to search for valid GC models. Valid models were those that satisfied 17 sub- and supra-threshold cellular-scale electrophysiological measurements from rat GCs. A vast majority (>99%) of the 15,000 random models were not electrophysiologically valid, demonstrating that arbitrarily random ion-channel combinations wouldn't yield GC functions. The 141 valid models (0.94% of 15,000) manifested heterogeneities in and cross-dependencies across local and propagating electrophysiological measurements, which matched with their respective biological counterparts. Importantly, these valid models were widespread throughout the parametric space and manifested weak cross-dependencies across different parameters. These observations together showed that GC physiology could neither be obtained by entirely random ion-channel combinations nor is there an entirely determined single parametric combination that satisfied all constraints. The complexity, the heterogeneities in measurement and parametric spaces, and degeneracy associated with GC physiology should be rigorously accounted for, while assessing GCs and their robustness under physiological and pathological conditions.

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Section: Introductionmentioning

confidence: 99%

Ion-channel degeneracy and heterogeneities in the emergence of signature physiological characteristics of dentate gyrus granule cells

Kumari,

Narayanan

2024

Preprint

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Controlling morpho-electrophysiological variability of neurons with detailed biophysical models

Arnaudon,

Reva,

Zbili

et al. 2023

iScience

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Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation

Isbister,

Ecker,

Pokorny

et al. 2023

Preprint

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In recent years, large-scale computational models of the cortex have emerged as a powerful way to study the multi-scale mechanisms of neural processing. However, due to computational costs and difficulty of parameterization, detailed biophysical reconstructions have so far been restricted to small volumes of tissue, where the study of macro- and meso-scale interactions that are central to cortical function is not possible. We describe here, and in a companion paper, an approach to address the scaling challenges and provide a model of multiple interacting cortical regions at a subcellular level of detail. The model consists of 4.2 million morphologically detailed neurons in 8 sub-regions and connected with 13.2 billion synapses through local and long-range connectivity. Its anatomical aspects are described in the companion paper; here, we introduce physiological models of neuronal activity and synaptic transmission that integrate a large number of literature sources and were built using previously published algorithms. Biological neuronal diversity was captured in 208 morpho-electrical neuron types, five types of synaptic short-term dynamics, and pathway-specificity of synaptic parameters. A representation of synaptic input from cortical regions not present in the model was added and efficiently calibrated to reference firing rates. The model exhibits a spectrum of dynamical states differing in the degree to which they are internally versus externally driven. We characterized which parts of the spectrum are compatible with available experimental data on layer-specific delays and amplitudes of responses to simple stimuli, and found anin vivo-like regime at the edge of a transition from asynchronous to synchronous spontaneous activity. We developed a rich set of simulation tools to recreate a diverse set of laboratory experimentsin silico, providing further validation and demonstrating the utility of the model in a variety of paradigms. Finally, we found that the large spatial scale of the model, that incorporates multiple cortical regions, led to the emergence of multiple independent computational units interacting through long-range synaptic pathways. The model provides a framework for the continued integration of experimental findings, for challenging hypotheses and making testable predictions, and provides a foundation for further simulation-based studies of cortical processing and learning.

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A universal workflow for creation, validation, and generalization of detailed neuronal models

Cited by 3 publications

References 62 publications

Ion-channel degeneracy and heterogeneities in the emergence of signature physiological characteristics of dentate gyrus granule cells

Ion-channel degeneracy and heterogeneities in the emergence of signature physiological characteristics of dentate gyrus granule cells

Controlling morpho-electrophysiological variability of neurons with detailed biophysical models

Modeling and Simulation of Neocortical Micro- and Mesocircuitry. Part II: Physiology and Experimentation

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