ArticlePLoS computational biology2009
Towards reproducible descriptions of neuronal network models.
Article in PLoS computational biology, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 97 papers.
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97 citing papers in PubMed, 181 citations in OpenAlex.
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- Metamodelling of a two-population spiking neural network.PLoS computational biology · 2023Article
- Analysis of Network Models with Neuron-Astrocyte Interactions.Neuroinformatics · 2023Article
- Biophysical parameters control signal transfer in spiking network.Frontiers in computational neuroscience · 2023Article
- Connectivity concepts in neuronal network modeling.PLoS computational biology · 2022Review
- Neuron-Glia Interactions and Brain Circuits.Advances in experimental medicine and biology · 2022Article
- Addressing skepticism of the critical brain hypothesis.Frontiers in computational neuroscience · 2022Review
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- Computation of the electroencephalogram (EEG) from network models of point neurons.PLoS computational biology · 2021Article
- Dynamical Characteristics of Recurrent Neuronal Networks Are Robust Against Low Synaptic Weight Resolution.Frontiers in neuroscience · 2021Article
- ConGen-A Simulator-Agnostic Visual Language for Definition and Generation of Connectivity in Large and Multiscale Neural Networks.Frontiers in neuroinformatics · 2021Article
37 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
3 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Progress in science depends on the effective exchange of ideas among scientists. New ideas can be assessed and criticized in a meaningful manner only if they are formulated precisely. This applies to simulation studies as well as to experiments and theories. But after more than 50 years of neuronal network simulations, we still lack a clear and common understanding of the role of computational models in neuroscience as well as established practices for describing network models in publications. This hinders the critical evaluation of network models as well as their re-use. We analyze here 14 research papers proposing neuronal network models of different complexity and find widely varying approaches to model descriptions, with regard to both the means of description and the ordering and placement of material. We further observe great variation in the graphical representation of networks and the notation used in equations. Based on our observations, we propose a good model description practice, composed of guidelines for the organization of publications, a checklist for model descriptions, templates for tables presenting model structure, and guidelines for diagrams of networks. The main purpose of this good practice is to trigger a debate about the communication of neuronal network models in a manner comprehensible to humans, as opposed to machine-readable model description languages. We believe that the good model description practice proposed here, together with a number of other recent initiatives on data-, model-, and software-sharing, may lead to a deeper and more fruitful exchange of ideas among computational neuroscientists in years to come. We further hope that work on standardized ways of describing--and thinking about--complex neuronal networks will lead the scientific community to a clearer understanding of high-level concepts in network dynamics, and will thus lead to deeper insights into the function of the brain.
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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.