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Published in: Journal of Computational Neuroscience 1/2017

15-09-2016

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

Authors: Robert A. McDougal, Thomas M. Morse, Ted Carnevale, Luis Marenco, Rixin Wang, Michele Migliore, Perry L. Miller, Gordon M. Shepherd, Michael L. Hines

Published in: Journal of Computational Neuroscience | Issue 1/2017

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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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Literature
go back to reference Andrews SS, Addy NJ, Brent R, Arkin AP (2010) Detailed simulations of cell biology with Smoldyn 2.1. PLoS Computational Biology, 6(3), e1000705. Andrews SS, Addy NJ, Brent R, Arkin AP (2010) Detailed simulations of cell biology with Smoldyn 2.1. PLoS Computational Biology, 6(3), e1000705.
go back to reference 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. (2006). Mobilizing the base of neuroscience data: the case of neuronal morphologies. Nature. Reviews in the Neurosciences, 7, 318–324.
go back to reference Ascoli, G. A., Donohue, D. E., & Halavi, M. (2007). NeuroMorpho.Org: a central resource for neuronal morphologies. The Journal of Neuroscience, 27, 9247–9251.CrossRefPubMed Ascoli, G. A., Donohue, D. E., & Halavi, M. (2007). NeuroMorpho.Org: a central resource for neuronal morphologies. The Journal of Neuroscience, 27, 9247–9251.CrossRefPubMed
go back to reference 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.CrossRefPubMed 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.CrossRefPubMed
go back to reference 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.CrossRefPubMedPubMedCentral 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.CrossRefPubMedPubMedCentral
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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. 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.
go back to reference Chung, K., & Deisseroth, K. (2013). CLARITY for mapping the nervous system. Nature methods, 10(6), 508–513.CrossRefPubMed Chung, K., & Deisseroth, K. (2013). CLARITY for mapping the nervous system. Nature methods, 10(6), 508–513.CrossRefPubMed
go back to reference 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. 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.
go back to reference 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.CrossRefPubMed 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.CrossRefPubMed
go back to reference Dodge, F. A., & Cooley, J. W. (1973). Action potential of the motorneuron. IBM Journal of Research and Development, 17, 219–229.CrossRef Dodge, F. A., & Cooley, J. W. (1973). Action potential of the motorneuron. IBM Journal of Research and Development, 17, 219–229.CrossRef
go back to reference 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. 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.
go back to reference 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.CrossRefPubMedPubMedCentral 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.CrossRefPubMedPubMedCentral
go back to reference 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.CrossRefPubMedCentral 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.CrossRefPubMedCentral
go back to reference 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.CrossRefPubMedPubMedCentral 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.CrossRefPubMedPubMedCentral
go back to reference Hines M (1993) NEURON—a program for simulation of nerve equations. In Neural systems: Analysis and modeling (pp. 127–136). New York: Springer. Hines M (1993) NEURON—a program for simulation of nerve equations. In Neural systems: Analysis and modeling (pp. 127–136). New York: Springer.
go back to reference 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.CrossRefPubMedPubMedCentral 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.CrossRefPubMedPubMedCentral
go back to reference 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.CrossRef 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.CrossRef
go back to reference Insel, T. R., Landis, S. C., & Collins, F. S. (2013). The NIH brain initiative. Science, 340. Insel, T. R., Landis, S. C., & Collins, F. S. (2013). The NIH brain initiative. Science, 340.
go back to reference Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14, 1569–1572.CrossRefPubMed Izhikevich, E. M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14, 1569–1572.CrossRefPubMed
go back to reference 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.CrossRefPubMed 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.CrossRefPubMed
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference 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 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).
go back to reference 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.CrossRefPubMed 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.CrossRefPubMed
go back to reference Lloyd, C. M., Lawson, J. R., Hunter, P. J., & Nielsen, P. F. (2008). The CellML model repository. Bioinformatics, 24, 2122–2123.CrossRefPubMed Lloyd, C. M., Lawson, J. R., Hunter, P. J., & Nielsen, P. F. (2008). The CellML model repository. Bioinformatics, 24, 2122–2123.CrossRefPubMed
go back to reference Mainen, Z. F., & Sejnowski, T. J. (1996). Influence of dendritic structure on firing pattern in model neocortical neurons. Nature, 382, 363–366.CrossRefPubMed Mainen, Z. F., & Sejnowski, T. J. (1996). Influence of dendritic structure on firing pattern in model neocortical neurons. Nature, 382, 363–366.CrossRefPubMed
go back to reference 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.CrossRefPubMedPubMedCentral 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.CrossRefPubMedPubMedCentral
go back to reference 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. 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.
go back to reference 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.CrossRefPubMedPubMedCentral 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.CrossRefPubMedPubMedCentral
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference 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.CrossRef 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.CrossRef
go back to reference Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012). Computational psychiatry. Trends in Cognitive Sciences, 16, 72–80.CrossRefPubMed Montague, P. R., Dolan, R. J., Friston, K. J., & Dayan, P. (2012). Computational psychiatry. Trends in Cognitive Sciences, 16, 72–80.CrossRefPubMed
go back to reference 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. 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.
go back to reference 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.CrossRefPubMedPubMedCentral 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.CrossRefPubMedPubMedCentral
go back to reference 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.CrossRef 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.CrossRef
go back to reference 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., 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.
go back to reference 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. 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.
go back to reference 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.CrossRefPubMedPubMedCentral 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.CrossRefPubMedPubMedCentral
go back to reference 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. (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.
go back to reference Rall, W., & Shepherd, G. M. (1968). Theoretical reconstruction of field potentials and dendrodendritic synaptic interactions in olfactory bulb. Journal of Neurophysiology, 31, 884–915.PubMed Rall, W., & Shepherd, G. M. (1968). Theoretical reconstruction of field potentials and dendrodendritic synaptic interactions in olfactory bulb. Journal of Neurophysiology, 31, 884–915.PubMed
go back to reference 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.CrossRefPubMed 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.CrossRefPubMed
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference 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 modeling Caenorhabditis elegans. Frontiers in Computational Neuroscience, 8, 137.CrossRefPubMedPubMedCentral 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 modeling Caenorhabditis elegans. Frontiers in Computational Neuroscience, 8, 137.CrossRefPubMedPubMedCentral
go back to reference Traub, R. D. (1977). Repetitive firing of Renshaw spinal interneurons. Biological Cybernetics, 27, 71–76.CrossRefPubMed Traub, R. D. (1977). Repetitive firing of Renshaw spinal interneurons. Biological Cybernetics, 27, 71–76.CrossRefPubMed
go back to reference Traub, R. D., & Llinas, R. (1977). The spatial distribution of ionic conductances in normal and axotomized motorneurons. Neuroscience, 2, 829–849.CrossRef Traub, R. D., & Llinas, R. (1977). The spatial distribution of ionic conductances in normal and axotomized motorneurons. Neuroscience, 2, 829–849.CrossRef
go back to reference 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.PubMed 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.PubMed
go back to reference 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.CrossRefPubMedPubMedCentral 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.CrossRefPubMedPubMedCentral
Metadata
Title
Twenty years of ModelDB and beyond: building essential modeling tools for the future of neuroscience
Authors
Robert A. McDougal
Thomas M. Morse
Ted Carnevale
Luis Marenco
Rixin Wang
Michele Migliore
Perry L. Miller
Gordon M. Shepherd
Michael L. Hines
Publication date
15-09-2016
Publisher
Springer US
Published in
Journal of Computational Neuroscience / Issue 1/2017
Print ISSN: 0929-5313
Electronic ISSN: 1573-6873
DOI
https://doi.org/10.1007/s10827-016-0623-7

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