- Robert A. McDougal1,
- Thomas M. Morse1,
- Ted Carnevale1,
- Luis Marenco1,2,3,
- Rixin Wang3,4,
- Michele Migliore1,5,
- Perry L. Miller2,3,4,
- Gordon M. Shepherd1 &
- …
- Michael L. Hines1
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137Citations
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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.
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References
Andrews SS, Addy NJ, Brent R, Arkin AP (2010) Detailed simulations of cell biology with Smoldyn 2.1.PLoS Computational Biology,6(3), e1000705.
Ascoli, G. A. (2006). Mobilizing the base of neuroscience data: the case of neuronal morphologies. Nature.Reviews in the Neurosciences, 7, 318–324.
Ascoli, G. A. (2015). Sharing neuron data: carrots, sticks, and digital records.PLoS Biology, 13(10), e1002275.
Ascoli, G. A., Donohue, D. E., & Halavi, M. (2007). NeuroMorpho.Org: a central resource for neuronal morphologies.The Journal of Neuroscience, 27, 9247–9251.
Baker, B. J., Kosmidis, E. K., Vucinic, D., Falk, C. X., Cohen, L. B., Djurisic, M., & Zecevic, D. (2005). Imaging brain activity with voltage-and calcium-sensitive dyes.Cellular and molecular neurobiology, 25(2), 245–282.
Barthó, P., Slézia, A., Varga, V., Bokor, H., Pinault, D., Buzsáki, G., & Acsády, L. (2007). Cortical control of zona incerta.Journal of Neuroscience, 27(7), 1670–1681.
Brandi, M., Brocke, E., Talukdar, H. A., Hanke, M., Bhalla, U. S., Kotaleski, J. H., & Djurfeldt, M. (2011). Connecting MOOSE and NeuroRD through MUSIC: towards a communication framework for multi-scale modeling.BMC Neuroscience, 12(Suppl 1), 77.
Carnevale, T., Majumdar, A., Sivagnanam, S., Yoshimoto, K., Astakhov, V., Bandrowski, A., & Martone, M. (2014). The neuroscience gateway portal: high performance computing made easy.BMC Neuroscience, 15.
Chen, F., Tillberg, P. W., & Boyden, E. S. (2015). Expansion microscopy.Science, 347(6221), 543–548.
Chung, K., & Deisseroth, K. (2013). CLARITY for mapping the nervous system.Nature methods, 10(6), 508–513.
Crook, SM, Davison, AP, Plesser, HE (2013) Learning from the past: approaches for reproducibility in computational neuroscience. 20 Years of Computational Neuroscience. Springer, New York 73–102.
Deisseroth, K. (2011). Optogenetics.Nature methods, 8(1), 26–29.
Destexhe, A., Mainen, Z. F., & Sejnowski, T. J. (1994). Synthesis of models for excitable membranes, synaptic transmission and neuromodulation using a common kinetic formalism.Journal of Computational Neuroscience, 1, 195–230.
Dodge, F. A., & Cooley, J. W. (1973). Action potential of the motorneuron.IBM Journal of Research and Development, 17, 219–229.
Eliasmith, C., Stewart, T. C., Choo, X., Bekolay, T., DeWolf, T., Tang, C., & Rasmussen, D. (2012). A large-scale model of the functioning brain.Science 338, 1202–1205.
Gleeson, P., Crook, S., Cannon, R. C., Hines, M. L., Billings, G. O., Farinella, M., Morse, T. M., Davison, A. P., Ray, S., Bhalla, U. S., & Barnes, S. R. (2010). NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail.PLoS Computational Biology, 6(6), e1000815.
Gleeson, P., Piasini, E., Crook, S., Cannon, R., Steuber, V., Jaeger, D., Solinas, S., D’Angelo, E., & Silver, R. A. (2012). The open source brain initiative: enabling collaborative modelling in computational neuroscience.BMC Neuroscience, 13(Suppl 1), O7.
Hamilton, D. J., Shepherd, G. M., Martone, M. E., & Ascoli, G. A. (2012). An ontological approach to describing neurons and their relationships.Front Neuroinform, 6, 15.
Hines M (1993) NEURON—a program for simulation of nerve equations. InNeural systems: Analysis and modeling (pp. 127–136). New York: Springer.
Hines, M. L., Morse, T., Migliore, M., Carnevale, N. T., & Shepherd, G. M. (2004). ModelDB: a database to support computational neuroscience.Journal of Computational Neuroscience, 17, 7–11.
Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve.J Physiol (Lond), 117, 500–544.
Insel, T. R., Landis, S. C., & Collins, F. S. (2013). The NIH brain initiative.Science, 340.
Izhikevich, E. M. (2003). Simple model of spiking neurons.IEEE Transactions on Neural Networks, 14, 1569–1572.
Kasthuri, N., Hayworth, K. J., Berger, D. R., Schalek, R. L., Conchello, J. A., Knowles-Barley, S., Lee, D., Vázquez-Reina, A., Kaynig, V., Jones, T. R., Roberts, M., Morgan, J. L., Tapia, J. C., Seung, H. S., Ronca, W. G., Vogelstein, J. T., Burns, R., Sussman, D. L., Priebe, C. E., Pfister, H., & Lichtman, J. W. (2015). Saturated reconstruction of a volume of neocortex.Cell, 162(3), 648–661.
Keller, D., Babai, N., Kochubey, O., Han, Y., Markram, H., Schürmann, F., & Schneggenburger, R. (2015). An exclusion zone for Ca2+ channels around docked vesicles explains release control by multiple channels at a CNS synapse.PLoS Computational Biology 11, e1004253.
Kim, J. K., & Forger, D. B. (2012). A mechanism for robust circadian timekeeping via stoichiometric balance.Molecular Systems Biology 8(630), 1–14. doi:10.1038/msb.2012.62.
Le Franc, Y., Davison, A. P., Gleeson, P., Imam, F. T., Kriener, B., Larson, S. D., Ray, S., Schwabe, L., Hill, S., & De Schutter, E. (2012). Computational neuroscience ontology: a new tool to provide semantic meaning to your models.BMC Neuroscience, 13(Suppl 1).
Le Novere, N., Bornstein, B., Broicher, A., Courtot, M., Donizelli, M., Dharuri, H., Li, L., Sauro, H., Schilstra, M., Shapiro, B., & Snoep, J. L. (2006). BioModels database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems.Nucleic Acids Research, 34, D689–D691.
Lloyd, C. M., Lawson, J. R., Hunter, P. J., & Nielsen, P. F. (2008). The CellML model repository.Bioinformatics, 24, 2122–2123.
Mainen, Z. F., & Sejnowski, T. J. (1996). Influence of dendritic structure on firing pattern in model neocortical neurons.Nature, 382, 363–366.
Man, O., Gilad, Y., & Lancet, D. (2004). Prediction of the odorant binding site of olfactory receptor proteins by human–mouse comparisons.Protein Science, 13(1), 240–254.
Markram, H. (2012). The human brain project.Scientific American, 306(6), 50–55.
Martin JB, Pechura CM eds (1991). Mapping the brain and its functions: integrating enabling technologies into Neuroscience Research (Vol. 91, No. 8). Washington, DC: National Academies Press.
McDougal, R. A., Morse, T. M., Hines, M. L., & Shepherd, G. M. (2015). ModelView for ModelDB: online presentation of model structure.Neuroinformatics, 13, 459–470.
McDougal, R. A., Bulanova, A. S., Lytton, W. W. (2016) Reproducibility in computational neuroscience models and simulations.IEEE Transactions on Biomedical Engineering. doi:10.1109/TBME.2016.2539602.
Migliore, M., Cavarretta, F., Hines, M. L., & Shepherd, G. M. (2014). Distributed organization of a brain microcircuit analysed by three-dimensional modeling: the olfactory bulb.Frontiers in Computational Neuroscience 8, 50.
Mirsky, J. S., Nadkarni, P. M., Healy, M. D., Miller, P. L., & Shepherd, G. M. (1998). Database tools for integrating and searching membrane property data correlated with neuronal morphology.Journal of Neuroscience Method, 82, 105–121.
Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012). Computational psychiatry.Trends in Cognitive Sciences, 16, 72–80.
Morse, T., Carnevale, N. T., Mutalik, P., Migliore, M., & Shepherd, G. M. (2010). Abnormal excitability of oblique dendrites implicated in early Alzheimer’s: a computational study.Front in Neural Circuits, 4, 16.
Nadkarni, P. M., Marenco, L., Chen, R., Skoufos, E., Shepherd, G., & Miller, P. (1999). Organization of heterogeneous scientific data using the EAV/CR representation.Journal of the American Medical Informatics Association, 6(6), 478–493.
Najafi, K., & Wise, K. D. (1986). An implantable multielectrode array with on-chip signal processing.IEEE Journal of Solid-State Circuits, 21(6), 1035–1044.
Neymotin, S. A., McDougal, R. A., Sherif, M. A., Fall, C. P., Hines, M. L., & Lytton, W. W. (2015). Neuronal calcium wave propagation varies with changes in endoplasmic reticulum parameters: a computer model.Neural Computation 27(4), 898–924.
Neymotin, S. A., McDougal, R. A., Bulanova, A. S., Zeki, M., Lakatos, P., Terman, D., Hines, M. L., & Lytton, W. W. (2016). Calcium regulation of HCN channels supports persistent activity in a multiscale model of neocortex.Neuroscience 316, 344–366.
Peterson, B. E., Healy, M. D., Nadkarni, P. M., Miller, P. L., & Shepherd, G. M. (1996). ModelDB: an environment for running and storing computational models and their results applied to neuroscience.Journal of the American Medical Informatics Association, 3, 389–398.
Rall, W. (1964). Theoretical significance of dendritic trees for neuronal input-output relations. In R. F. Reiss (Ed.),Neural Theory and Modeling (pp. 73–97). Stanford, CA: Stanford University Press.
Rall, W., & Shepherd, G. M. (1968). Theoretical reconstruction of field potentials and dendrodendritic synaptic interactions in olfactory bulb.Journal of Neurophysiology, 31, 884–915.
Shepherd, G. M., & Brayton, R. K. (1979). Computer simulation of a dendrodendritic synaptic circuit for self-and lateral-inhibition in the olfactory bulb.Brain Research, 175, 377–382.
Sivagnanam, S., Majumdar, A., Yoshimoto, K., Astakhov, V., Bandrowski, A., Martone, M. E., & Carnevale, N. T. (2013)Introducing the Neuroscience Gateway, IWSG, volume 993 of CEUR Workshop Proceedings, CEUR-WS.org. London, UK.
Stiles, J. R., & Bartol, T. M. (2001). Monte Carlo methods for simulating realistic synaptic microphysiology using MCell.Computational Neuroscience: Realistic Modeling for Experimentalists, 87–127.
Szigeti, B., Gleeson, P., Vella, M., Khayrulin, S., Palyanov, A., Hokanson, J., Currie, M., Cantarelli, M., Idili, G., & Larson, S. (2014). OpenWorm: an open-science approach to modelingCaenorhabditis elegans.Frontiers in Computational Neuroscience, 8, 137.
Traub, R. D. (1977). Repetitive firing of Renshaw spinal interneurons.Biological Cybernetics, 27, 71–76.
Traub, R. D., & Llinas, R. (1977). The spatial distribution of ionic conductances in normal and axotomized motorneurons.Neuroscience, 2, 829–849.
Traub, R. D., & Llinas, R. (1979). Hippocampal pyramidal cells: significance of dendritic ionic conductances for neuronal function and epileptogenesis.Journal of Neurophysiology, 42, 476–496.
Tripathy, S. J., Savitskaya, J., Burton, S. D., Urban, N. N., & Gerkin, R. C. (2014). NeuroElectro: a window to the world’s neuron electrophysiology data.Front Neuroinform, 8, 40.
Wang, X. J., & Krystal, J. H. (2014). Computational psychiatry.Neuron, 84(3), 638–654.
Yavuz, E., Turner, J., & Nowotny, T. (2016). GeNN: a code generation framework for accelerated brain simulations.Scientific Reports, 6, 18854.
Acknowledgments
This project was supported by the National Institutes of Health (NIH) grants R01 DC009977 from the National Institute on Deafness and Other Communication Disorders (NIDCD) and T15 LM007056 from the National Library of Medicine (NLM). We are grateful for platforms provided to us for the curation of ModelDB models including the Louise cluster (part of Yale’s HPC facilities operated by the Yale Center for Research Computing and Yale’s W.M. Keck Biotechnology Laboratory, funded by NIH grants: RR19895 and RR029676-01) and the Neuroscience Gateway (NSG) Portal (Sivagnanam et al.2013) supported by the National Science Foundation.
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Department of Neuroscience, Yale University, PO Box 208001, New Haven, CT, 06520-8001, USA
Robert A. McDougal, Thomas M. Morse, Ted Carnevale, Luis Marenco, Michele Migliore, Gordon M. Shepherd & Michael L. Hines
VA Connecticut Healthcare System, West Haven, CT, 06516, USA
Luis Marenco & Perry L. Miller
Center for Medical Informatics, Yale University, New Haven, CT, 06520, USA
Luis Marenco, Rixin Wang & Perry L. Miller
Department of Anesthesiology, Yale University, New Haven, CT, 06520, USA
Rixin Wang & Perry L. Miller
Institute of Biophysics, National Research Council, Palermo, Italy
Michele Migliore
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Robert A. McDougal and Thomas M. Morse contributed equally to the development of this work.
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McDougal, R.A., Morse, T.M., Carnevale, T.et al. Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience.J Comput Neurosci42, 1–10 (2017). https://doi.org/10.1007/s10827-016-0623-7
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