ArticleNeural computation2021
NetPyNE Implementation and Scaling of the Potjans-Diesmann Cortical Microcircuit Model.
Article in Neural computation, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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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
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Who cites it
6 citing papers in PubMed, 7 citations in OpenAlex.
- New directions for complex systems in contemporary neuroscience: a morphodynamic and emergent function approach.Frontiers in computational neuroscience · 2026Review
- Building on models-a perspective for computational neuroscience.Cerebral cortex (New York, N.Y. : 1991) · 2025Article
- Connectivity concepts in neuronal network modeling.PLoS computational biology · 2022Review
- Modernizing the NEURON Simulator for Sustainability, Portability, and Performance.Frontiers in neuroinformatics · 2022Article
- Large-scale biophysically detailed model of somatosensory thalamocortical circuits in NetPyNE.Frontiers in neuroinformatics · 2022Article
- Unique scales preserve self-similar integrate-and-fire functionality of neuronal clusters.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
Abstract
The Potjans-Diesmann cortical microcircuit model is a widely used model originally implemented in NEST. Here, we reimplemented the model using NetPyNE, a high-level Python interface to the NEURON simulator, and reproduced the findings of the original publication. We also implemented a method for scaling the network size that preserves first- and second-order statistics, building on existing work on network theory. Our new implementation enabled the use of more detailed neuron models with multicompartmental morphologies and multiple biophysically realistic ion channels. This opens the model to new research, including the study of dendritic processing, the influence of individual channel parameters, the relation to local field potentials, and other multiscale interactions. The scaling method we used provides flexibility to increase or decrease the network size as needed when running these CPU-intensive detailed simulations. Finally, NetPyNE facilitates modifying or extending the model using its declarative language; optimizing model parameters; running efficient, large-scale parallelized simulations; and analyzing the model through built-in methods, including local field potential calculation and information flow measures.
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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.