ArticleNeuroinformatics2009
NETMORPH: a framework for the stochastic generation of large scale neuronal networks with realistic neuron morphologies.
Article in Neuroinformatics, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 75 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
75 citing papers in PubMed, 189 citations in OpenAlex.
- Synthetic Data Generation for Classifying Electrophysiological and Morpho-Electrophysiological Neurons from Mouse Visual Cortex.Neuroinformatics · 2025Article
- Article
- Role of connectivity anisotropies in the dynamics of cultured neuronal networks.PLoS computational biology · 2025Article
- Article
- Generating brain-wide connectome using synthetic axonal morphologies.Nature communications · 2025Article
- Neurons exploit stochastic growth to rapidly and economically build dense dendritic arbors.Nature communications · 2025Article
- Article
- Computational Generation of Long-range Axonal Morphologies.Neuroinformatics · 2025Article
- A biologically inspired repair mechanism for neuronal reconstructions with a focus on human dendrites.PLoS computational biology · 2024Article
- Controlling morpho-electrophysiological variability of neurons with detailed biophysical models.iScience · 2023Article
- Models of Purkinje cell dendritic tree selection during early cerebellar development.PLoS computational biology · 2023Article
- Comparing the segmentation of quantitative phase images of neurons using convolutional neural networks trained on simulated and augmented imagery.Neurophotonics · 2023Article
- Tissues as networks of cells: towards generative rules of complex organ development.Journal of the Royal Society, Interface · 2023Review
- Brain-wide dendrites in a near-optimal performance of dynamic range and information transmission.Scientific reports · 2023Article
- Efficient simulation of neural development using shared memory parallelization.Frontiers in neuroinformatics · 2023Article
- Active Dendrites and Local Field Potentials: Biophysical Mechanisms and Computational Explorations.Neuroscience · 2022Review
- Efficient metadata mining of web-accessible neural morphologies.Progress in biophysics and molecular biology · 2022Article
- Creative Destruction: A Basic Computational Model of Cortical Layer Formation.Cerebral cortex (New York, N.Y. : 1991) · 2021Article
- Prediction of Neural Diameter From Morphology to Enable Accurate Simulation.Frontiers in neuroinformatics · 2021Article
- Achieving functional neuronal dendrite structure through sequential stochastic growth and retraction.eLife · 2020Article
15 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
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We present a simulation framework, called NETMORPH, for the developmental generation of 3D large-scale neuronal networks with realistic neuron morphologies. In NETMORPH, neuronal morphogenesis is simulated from the perspective of the individual growth cone. For each growth cone in a growing axonal or dendritic tree, its actions of elongation, branching and turning are described in a stochastic, phenomenological manner. In this way, neurons with realistic axonal and dendritic morphologies, including neurite curvature, can be generated. Synapses are formed as neurons grow out and axonal and dendritic branches come in close proximity of each other. NETMORPH is a flexible tool that can be applied to a wide variety of research questions regarding morphology and connectivity. Research applications include studying the complex relationship between neuronal morphology and global patterns of synaptic connectivity. Possible future developments of NETMORPH are discussed.
Indexed as
Identifiers
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.