ArticleFrontiers in neural circuits2012
Toward a full-scale computational model of the rat dentate gyrus.
Article in Frontiers in neural circuits, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.
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Who cites it
27 citing papers in PubMed.
- DeltaQ: Value-Guided Hebbian Learning in Spiking Neuronal Networks for Multi-Goal Navigation.bioRxiv : the preprint server for biology · 2026Article
- How to train your neuron: developing a detailed, up-to-date, multipurpose model of hippocampal CA1 pyramidal cells.Frontiers in computational neuroscience · 2026Article
- A realistic morpho-anatomical connection strategy for modelling full-scale point-neuron microcircuits.Scientific reports · 2022Article
- Spiking Neural Networks and Hippocampal Function: A Web-Accessible Survey of Simulations, Modeling Methods, and Underlying Theories.Cognitive systems research · 2021Article
- Ion-channel regulation of response decorrelation in a heterogeneous multi-scale model of the dentate gyrus.Current research in neurobiology · 2021Article
- HippoUnit: A software tool for the automated testing and systematic comparison of detailed models of hippocampal neurons based on electrophysiological data.PLoS computational biology · 2021Article
- Research on Network Model of Dentate Gyrus Based on Bionics.Journal of healthcare engineering · 2021Article
- Article
- Topographic Organization of Correlation Along the Longitudinal and Transverse Axes in Rat Hippocampal CA3 Due to Excitatory Afferents.Frontiers in computational neuroscience · 2020Article
- Generation of Granule Cell Dendritic Morphologies by Estimating the Spatial Heterogeneity of Dendritic Branching.Frontiers in computational neuroscience · 2020Article
- Disparate forms of heterogeneities and interactions among them drive channel decorrelation in the dentate gyrus: Degeneracy and dominance.Hippocampus · 2019Article
- Memory Prosthesis: Is It Time for a Deep Neuromimetic Computing Approach?Frontiers in neuroscience · 2019Article
- Point Process Filtering Estimates of Branching Rate for Neural Dendritic Morphology Generation.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2018Article
- Article
- Network neuroscience.Nature neuroscience · 2017Review
- Automating NEURON Simulation Deployment in Cloud Resources.Neuroinformatics · 2017Article
- Distinct current modules shape cellular dynamics in model neurons.Neuroscience · 2016Article
- Computational Modeling of Adult Neurogenesis.Cold Spring Harbor perspectives in biology · 2016Review
- Microcircuits in Epilepsy: Heterogeneity and Hub Cells in Network Synchronization.Cold Spring Harbor perspectives in medicine · 2015Review
- Future of seizure prediction and intervention: closing the loop.Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society · 2015Review
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3 authors.
Funding
Abstract
Recent advances in parallel computing, including the creation of the parallel version of the NEURON simulation environment, have allowed for a previously unattainable level of complexity and detail in neural network models. Previously, we published a functional NEURON model of the rat dentate gyrus with over 50,000 biophysically realistic, multicompartmental neurons, but network simulations could only utilize a single processor. By converting the model to take advantage of parallel NEURON, we are now able to utilize greater computational resources and are able to simulate the full-scale dentate gyrus, containing over a million neurons. This has eliminated the previous necessity for scaling adjustments and allowed for a more direct comparison to experimental techniques and results. The translation to parallel computing has provided a superlinear speedup of computation time and dramatically increased the overall computer memory available to the model. The incorporation of additional computational resources has allowed for more detail and elements to be included in the model, bringing the model closer to a more complete and accurate representation of the biological dentate gyrus. As an example of a major step toward an increasingly accurate representation of the biological dentate gyrus, we discuss the incorporation of realistic granule cell dendrites into the model. Our previous model contained simplified, two-dimensional dendritic morphologies that were identical for neurons of the same class. Using the software tools L-Neuron and L-Measure, we are able to introduce cell-to-cell variability by generating detailed, three-dimensional granule cell morphologies that are based on biological reconstructions. Through these and other improvements, we aim to construct a more complete full-scale model of the rat dentate gyrus, to provide a better tool to delineate the functional role of cell types within the dentate gyrus and their pathological changes observed in epilepsy.
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