ArticleJournal of mathematical neuroscience2019
Efficient calculation of heterogeneous non-equilibrium statistics in coupled firing-rate models.
Article in Journal of mathematical neuroscience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Differences in olfactory bulb mitral cell spiking with ortho- and retronasal stimulation revealed by data-driven models.PLoS computational biology · 2021Article
- Neural Systems Under Change of Scale.Frontiers in computational neuroscience · 2021Article
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3 authors.
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Abstract
Understanding nervous system function requires careful study of transient (non-equilibrium) neural response to rapidly changing, noisy input from the outside world. Such neural response results from dynamic interactions among multiple, heterogeneous brain regions. Realistic modeling of these large networks requires enormous computational resources, especially when high-dimensional parameter spaces are considered. By assuming quasi-steady-state activity, one can neglect the complex temporal dynamics; however, in many cases the quasi-steady-state assumption fails. Here, we develop a new reduction method for a general heterogeneous firing-rate model receiving background correlated noisy inputs that accurately handles highly non-equilibrium statistics and interactions of heterogeneous cells. Our method involves solving an efficient set of nonlinear ODEs, rather than time-consuming Monte Carlo simulations or high-dimensional PDEs, and it captures the entire set of first and second order statistics while allowing significant heterogeneity in all model parameters.
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