The Importance of Standards for Sharing of Computational Models and Data
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Anderson, J. R., Bothell, D., Byrne, M. D., Douglass, S., Lebiere, C., & Qin, Y. (2004). An integrated theory of the mind. Psychological Review, 111, 1036–1060.
Baumgaertner, B., Devezer, B., Buzbas, E.O., and Nardin, L.G. (2018). A model-centric analysis of openness, replication, and reproducibility.
Bekolay, T., Bergstra, J., Hunsberger, E., Dewolf, T., Stewart, T. C., Rasmussen, D., Choo, X., Voelker, A. R., & Eliasmith, C. (2014). Nengo: A python tool for building large-scale functional brain models. Frontiers in Neuroinformatics, 7, 48.
Benureau, F. C. Y., & Rougier, N. P. (2017). Re-run, repeat, reproduce, reuse, replicate: Transforming code into scientific contributions. Frontiers in Neuroinformatics, 11, 69.
Buckheit, J.B., and Donoho, D.L. (1995). WaveLab and reproducible research. In Wavelets and statistics, A. Antoniadis, and G. Oppenheim, eds. (New York, NY: Springer New York), pp. 55–81.
Cannon, R. C., Gleeson, P., Crook, S., Ganapathy, G., Marin, B., Piasini, E., & Silver, R. A. (2014). LEMS: A language for expressing complex biological models in concise and hierarchical form and its use in underpinning NeuroML 2. Frontiers in Neuroinformatics, 8, 79.
Donkin, C., Brown, S., Heathcote, A., & Wagenmakers, E.-J. (2011). Diffusion versus linear ballistic accumulation: Different models but the same conclusions about psychological processes? Psychonomic Bulletin & Review, 18, 61–69.
Eglen, S. J., Marwick, B., Halchenko, Y. O., Hanke, M., Sufi, S., Gleeson, P., Silver, R. A., Davison, A. P., Lanyon, L., Abrams, M., Wachtler, T., Willshaw, D. J., Pouzat, C., & Poline, J. B. (2017). Toward standard practices for sharing computer code and programs in neuroscience. Nature Neuroscience, 20, 770–773.
Frank, M. J. (2015). Linking across levels of computation in model-based cognitive neuroscience. An introduction to model-based cognitive neuroscience (pp. 159–177).
Fum, D., Del Missier, F., and Stocco, A. (2007). The cognitive modeling of human behavior: Why a model is (sometimes) better than 10,000 words.
Gleeson, P., Davison, A. P., Silver, R. A., & Ascoli, G. A. (2017). A commitment to open source in neuroscience. Neuron, 96, 964–965.
Goldfarb, S., Leonard, N. E., Simen, P., Caicedo-Núñez, C. H., & Holmes, P. (2014). A comparative study of drift diffusion and linear ballistic accumulator models in a reward maximization perceptual choice task. Frontiers in Neuroscience, 8, 148.
Gorgolewski, K. J., Auer, T., Calhoun, V. D., Craddock, R. C., Das, S., Duff, E. P., Flandin, G., Ghosh, S. S., Glatard, T., Halchenko, Y. O., Handwerker, D. A., Hanke, M., Keator, D., Li, X., Michael, Z., Maumet, C., Nichols, B. N., Nichols, T. E., Pellman, J., Poline, J. B., Rokem, A., Schaefer, G., Sochat, V., Triplett, W., Turner, J. A., Varoquaux, G., & Poldrack, R. A. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3, 160044.
Navarro, D. J., & Fuss, I. G. (2009). Fast and accurate calculations for first-passage times in Wiener diffusion models. Journal of Mathematical Psychology, 53, 222–230.
Ratcliff, R., & Childers, R. (2015). Individual differences and fitting methods for the two-choice diffusion model of decision making. Decision, 2, 237–279.
Sanz Leon, P., Knock, S. A., Woodman, M. M., Domide, L., Mersmann, J., McIntosh, A. R., & Jirsa, V. (2013). The Virtual Brain: A simulator of primate brain network dynamics. Frontiers in Neuroinformatics, 7, 10.