ArticlePLoS computational biology2020
Simple models including energy and spike constraints reproduce complex activity patterns and metabolic disruptions.
Article in PLoS computational biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Unveiling the role of local metabolic constraints on the structure and activity of spiking neural networks.PLoS computational biology · 2025Article
- Metabolic implications of axonal demyelination and its consequences for synchronized network activity: AnJournal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism · 2023Article
- Cognition is entangled with metabolism: relevance for resting-state EEG-fMRI.Frontiers in human neuroscience · 2023Article
- Classification of bursting patterns: A tale of two ducks.PLoS computational biology · 2022Review
- Artificial neurovascular network (ANVN) to study the accuracy vs. efficiency trade-off in an energy dependent neural network.Scientific reports · 2021Article
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2 authors.
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Abstract
In this work, we introduce new phenomenological neuronal models (eLIF and mAdExp) that account for energy supply and demand in the cell as well as the inactivation of spike generation how these interact with subthreshold and spiking dynamics. Including these constraints, the new models reproduce a broad range of biologically-relevant behaviors that are identified to be crucial in many neurological disorders, but were not captured by commonly used phenomenological models. Because of their low dimensionality eLIF and mAdExp open the possibility of future large-scale simulations for more realistic studies of brain circuits involved in neuronal disorders. The new models enable both more accurate modeling and the possibility to study energy-associated disorders over the whole time-course of disease progression instead of only comparing the initially healthy status with the final diseased state. These models, therefore, provide new theoretical and computational methods to assess the opportunities of early diagnostics and the potential of energy-centered approaches to improve therapies.
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