Brette R, Goodman DF. (2011). Vectorized algorithms for spiking neural network simulation. Neural computation. 23 [PubMed]
Djurfeldt M. (2012). The connection-set algebra--a novel formalism for the representation of connectivity structure in neuronal network models. Neuroinformatics. 10 [PubMed]
Gewaltig MO, Cannon R. (2014). Current practice in software development for computational neuroscience and how to improve it. PLoS computational biology. 10 [PubMed]
Gleeson P et al. (2010). NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail. PLoS computational biology. 6 [PubMed]
Gleeson P, Steuber V, Silver RA. (2007). neuroConstruct: a tool for modeling networks of neurons in 3D space. Neuron. 54 [PubMed]
Manninen T, Havela R, Linne M-L. (2017). Reproducibility and comparability of computational models for astrocyte calcium excitability Front. Neuroinform..
McDougal RA, Bulanova AS, Lytton WW. (2016). Reproducibility in Computational Neuroscience Models and Simulations. IEEE transactions on bio-medical engineering. 63 [PubMed]
McDougal RA, Hines ML, Lytton WW. (2013). Reaction-diffusion in the NEURON simulator. Frontiers in neuroinformatics. 7 [PubMed]
Ray S, Bhalla US. (2008). PyMOOSE: Interoperable Scripting in Python for MOOSE. Frontiers in neuroinformatics. 2 [PubMed]
Sterratt DC, Graham B, Gillies A, Willshaw D. (2011). Principles of Computational Modelling in Neuroscience, Cambridge University Press.
Versace M, Ames H, Léveillé J, Fortenberry B, Gorchetchnikov A. (2008). KInNeSS: a modular framework for computational neuroscience. Neuroinformatics. 6 [PubMed]