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J Comput Neurosci. 2017 Feb;42(1):1-10. doi: 10.1007/s10827-016-0623-7. Epub 2016 Sep 15.

Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience.

Author information

1
Department of Neuroscience, Yale University, PO Box 208001, New Haven, CT, 06520-8001, USA. robert.mcdougal@yale.edu.
2
Department of Neuroscience, Yale University, PO Box 208001, New Haven, CT, 06520-8001, USA.
3
VA Connecticut Healthcare System, West Haven, CT, 06516, USA.
4
Center for Medical Informatics, Yale University, New Haven, CT, 06520, USA.
5
Department of Anesthesiology, Yale University, New Haven, CT, 06520, USA.
6
Institute of Biophysics, National Research Council, Palermo, Italy.

Abstract

Neuron modeling may be said to have originated with the Hodgkin and Huxley action potential model in 1952 and Rall's models of integrative activity of dendrites in 1964. Over the ensuing decades, these approaches have led to a massive development of increasingly accurate and complex data-based models of neurons and neuronal circuits. ModelDB was founded in 1996 to support this new field and enhance the scientific credibility and utility of computational neuroscience models by providing a convenient venue for sharing them. It has grown to include over 1100 published models covering more than 130 research topics. It is actively curated and developed to help researchers discover and understand models of interest. ModelDB also provides mechanisms to assist running models both locally and remotely, and has a graphical tool that enables users to explore the anatomical and biophysical properties that are represented in a model. Each of its capabilities is undergoing continued refinement and improvement in response to user experience. Large research groups (Allen Brain Institute, EU Human Brain Project, etc.) are emerging that collect data across multiple scales and integrate that data into many complex models, presenting new challenges of scale. We end by predicting a future for neuroscience increasingly fueled by new technology and high performance computation, and increasingly in need of comprehensive user-friendly databases such as ModelDB to provide the means to integrate the data for deeper insights into brain function in health and disease.

KEYWORDS:

Model analysis; Model discovery; Model sharing; Neuroinformatics; Reproducibility

PMID:
27629590
PMCID:
PMC5279891
[Available on 2018-02-01]
DOI:
10.1007/s10827-016-0623-7
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