ArticleThe Journal of neuroscience : the official journal of the Society for Neuroscience2017
Dynamical Timescale Explains Marginal Stability in Excitability Dynamics.
Article in The Journal of neuroscience : the official journal of the Society for Neuroscience, 2017. 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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Who cites it
5 citing papers in PubMed.
- Antifragile control systems in neuronal processing: a sensorimotor perspective.Biological cybernetics · 2025Review
- Multiscale effective connectivity analysis of brain activity using neural ordinary differential equations.PloS one · 2024Article
- A biophysical perspective on the resilience of neuronal excitability across timescales.Nature reviews. Neuroscience · 2023Review
- Distinct Structure of Cortical Population Activity on Fast and Infraslow Timescales.Cerebral cortex (New York, N.Y. : 1991) · 2019Article
- Cellular function given parametric variation in the Hodgkin and Huxley model of excitability.Proceedings of the National Academy of Sciences of the United States of America · 2018Article
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2 authors.
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Abstract
Action potentials, taking place over milliseconds, are the basis of neural computation. However, the dynamics of excitability over longer, behaviorally relevant timescales remain underexplored. A recent experiment used long-term recordings from single neurons to reveal multiple timescale fluctuations in response to constant stimuli, along with more reliable responses to variable stimuli. Here, we demonstrate that this apparent paradox is resolved if neurons operate in a marginally stable dynamic regime, which we reveal using a novel inference method. Excitability in this regime is characterized by large fluctuations while retaining high sensitivity to external varying stimuli. A new model with a dynamic recovery timescale that interacts with excitability captures this dynamic regime and predicts the neurons' response with high accuracy. The model explains most experimental observations under several stimulus statistics. The compact structure of our model permits further exploration on the network level.
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