ArticleScientific reports2012
Fast and accurate low-dimensional reduction of biophysically detailed neuron models.
Article in Scientific reports, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A connectome and analysis of the adulteLife · 2020Pooled it
- Introducing the Dendrify framework for incorporating dendrites to spiking neural networks.Nature communications · 2023Article
- Ultrafast simulation of large-scale neocortical microcircuitry with biophysically realistic neurons.eLife · 2022Article
- Bridging Hierarchies in Multi-Scale Models of Neural Systems: Look-Up Tables Enable Computationally Efficient Simulations of Non-linear Synaptic Dynamics.Frontiers in computational neuroscience · 2021Article
- An efficient analytical reduction of detailed nonlinear neuron models.Nature communications · 2020Article
- Topographic Organization of Correlation Along the Longitudinal and Transverse Axes in Rat Hippocampal CA3 Due to Excitatory Afferents.Frontiers in computational neuroscience · 2020Article
- Optimal solid state neurons.Nature communications · 2019Article
- Axonal Anatomy Optimizes Spatial Encoding in the Rat Entorhinal-Dentate System: A Computational Study.IEEE transactions on bio-medical engineering · 2019Article
- Network Activity Due to Topographic Organization of Schaffer Collaterals in a Large-Scale Model of Rat CA1.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2019Article
- Complex Electroresponsive Dynamics in Olivocerebellar Neurons Represented With Extended-Generalized Leaky Integrate and Fire Models.Frontiers in computational neuroscience · 2019Article
- Complex Dynamics in Simplified Neuronal Models: Reproducing Golgi Cell Electroresponsiveness.Frontiers in neuroinformatics · 2018Article
- Automatic Construction of Predictive Neuron Models through Large Scale Assimilation of Electrophysiological Data.Scientific reports · 2016Article
- Modeling the Cerebellar Microcircuit: New Strategies for a Long-Standing Issue.Frontiers in cellular neuroscience · 2016Review
- Simulation of alcohol action upon a detailed Purkinje neuron model and a simpler surrogate model that runs >400 times faster.BMC neuroscience · 2015Article
- Silicon central pattern generators for cardiac diseases.The Journal of physiology · 2015Review
- Computational neurobiology is a useful tool in translational neurology: the example of ataxia.Frontiers in neuroscience · 2015Article
- Using Strahler's analysis to reduce up to 200-fold the run time of realistic neuron models.Scientific reports · 2013Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Realistic modeling of neurons are quite successful in complementing traditional experimental techniques. However, their networks require a computational power beyond the capabilities of current supercomputers, and the methods used so far to reduce their complexity do not take into account the key features of the cells nor critical physiological properties. Here we introduce a new, automatic and fast method to map realistic neurons into equivalent reduced models running up to > 40 times faster while maintaining a very high accuracy of the membrane potential dynamics during synaptic inputs, and a direct link with experimental observables. The mapping of arbitrary sets of synaptic inputs, without additional fine tuning, would also allow the convenient and efficient implementation of a new generation of large-scale simulations of brain regions reproducing the biological variability observed in real neurons, with unprecedented advances to understand higher brain functions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.