ArticleeLife2019
NetPyNE, a tool for data-driven multiscale modeling of brain circuits.
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
93 citing papers in PubMed.
- Context-Aware Evidence-Gated Plasticity for Multi-Goal Learning in Spiking Neural Networks.Research square · 2026Article
- An in silico framework for dissecting the mechanistic origins of in vivo recorded neuronal activity.PLoS computational biology · 2026Article
- Linking functional and structural dendritic spine remodeling during fear learning and extinction in vivo.Science advances · 2026Article
- Context-Aware Evidence-Gated Plasticity for Multi-Goal Learning in Spiking Neural Networks.bioRxiv : the preprint server for biology · 2026Article
- Distinct cell-type contributions and network topography of theta-nested gamma oscillations in the medial entorhinal cortex.bioRxiv : the preprint server for biology · 2026Article
- Uncovering putative neural mechanisms of neurotherapeutic impacts on EEG using the Human Neocortical Neurosolver.bioRxiv : the preprint server for biology · 2026Article
- Spinal Processing of Spatiotemporally Diverse Tactile Stimuli: Computational Insights into Allodynia and Spinal Cord Stimulation.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- Testing circuit-level theories of consciousness in humans.Trends in cognitive sciences · 2026Review
- Optimizing electrode placement and information capacity for local field potentials in cortex.NeuroImage · 2026Article
- Article
- Bridging Model and Experiment in Systems Neuroscience with Cleo: The Closed-Loop, Electrophysiology, and Optophysiology Simulation Testbed.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2026Article
- NEURONpyxl: fast, flexible, Python-integrated simulation of biophysical neural networks with complex plastic synapses.Frontiers in computational neuroscience · 2026Article
- TVB C++: A Fast and Flexible Back-End for The Virtual Brain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Article
- JAXLEY: differentiable simulation enables large-scale training of detailed biophysical models of neural dynamics.Nature methods · 2025Article
- Differentiable simulation expands frontiers for biophysical neural models.Nature methods · 2025Article
- WhenAPL bioengineering · 2025Review
- Article
- Fast spiking interneurons autonomously generate fast gamma oscillations in the medial entorhinal cortex with excitation strength tuning ING-PING transitions.bioRxiv : the preprint server for biology · 2025Article
- Bridging model and experiment in systems neuroscience with Cleo: the Closed-Loop, Electrophysiology, and Optophysiology simulation testbed.bioRxiv : the preprint server for biology · 2025Article
33 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis - connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.
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What OpenQuestion holds
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.