Uncertainpy: A Python Toolbox for Uncertainty Quantification and Sensitivity Analysis in Computational Neuroscience

Simen Tennøe1,2, Geir Halnes1,3, Gaute T. Einevoll1,4,3
1Centre for Integrative Neuroplasticity, University of Oslo, Oslo, Norway
2Department of Informatics, University of Oslo, Oslo, Norway
3Faculty of Science and Technology, Norwegian University of Life Sciences, Ås, Norway
4Department of Physics, University of Oslo, Oslo, Norway

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Achard, 2006, Complex parameter landscape for a complex neuron model, PLoS Comput. Biol., 2, e94, 10.1371/journal.pcbi.0020094

Allken, 2014, The subcellular distribution of T-type Ca2+ channels in interneurons of the lateral geniculate nucleus, PLoS ONE, 10.1371/journal.pone.0107780

Babtie, 2017, How to deal with parameters for whole-cell modelling, J. R. Soc Interface, 14, 10.1098/rsif.2017.0237

Bahl, 2012, Automated optimization of a reduced layer 5 pyramidal cell model based on experimental data, J. Neurosci. Methods, 210, 22, 10.1016/j.jneumeth.2012.04.006

Beck, 1987, Water quality modeling: a review of the analysis of uncertainty, Water Resour. Res., 23, 1393, 10.1029/WR023i008p01393

Beer, 1999, Evolution and analysis of model CPGs for walking: II. General principles and individual variability, J. Comput. Neurosci., 7, 119, 10.1023/A:1008920021246

Bhalla, 1993, Exploring parameter space in detailed single neuron models: simulations of the mitral and granule cells of the olfactory bulb, J. Neurophysiol., 69, 1948, 10.1152/jn.1993.69.6.1948

Blomquist, 2009, Estimation of thalamocortical and intracortical network models from joint thalamic single-electrode and cortical laminar-electrode recordings in the rat barrel system, PLoS Comput. Biol., 5, e1000328, 10.1371/journal.pcbi.1000328

Blot, 2014, Ultra-rapid axon-axon ephaptic inhibition of cerebellar Purkinje cells by the pinceau, Nat. Neurosci., 17, 289, 10.1038/nn.3624

efel2015

Borgonovo, 2016, Sensitivity analysis: a review of recent advances, Eur. J. Oper. Res., 248, 869, 10.1016/j.ejor.2015.06.032

Brodland, 2015, How computational models can help unlock biological systems, Semin. Cell Dev. Biol., 62, 10.1016/j.semcdb.2015.07.001

Brunel, 2000, Dynamics of sparsely connected networks of excitatory and inhibitory spiking neurons, J. Comput. Neurosci., 8, 183, 10.1023/A:1008925309027

Campolongo, 2007, An effective screening design for sensitivity analysis of large models, Environ. Model. Softw., 22, 1509, 10.1016/j.envsoft.2006.10.004

Collette, 2013, Python and HDF5

Crestaux, 2009, Polynomial chaos expansion for sensitivity analysis, Reliabil. Eng. Syst. Saf., 94, 1161, 10.1016/j.ress.2008.10.008

Dayan, 2001, Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems

De Schutter, 1994, An active membrane model of the cerebellar Purkinje cell II. Simulation of synaptic responses, J. Neurophysiol., 71, 401, 10.1152/jn.1994.71.1.401

Degenring, 2004, Sensitivity analysis for the reduction of complex metabolism models, J. Process Control, 14, 729, 10.1016/j.jprocont.2003.12.008

Dragly, 2018, Experimental directory structure (exdir): an alternative to hdf5 without introducing a new file format, Front. Neuroinformatics, 12, 16, 10.3389/fninf.2018.00016

Druckmann, 2007, A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data, Front. Neurosci., 1, 7, 10.3389/neuro.01.1.1.001.2007

Eck, 2016, A guide to uncertainty quantification and sensitivity analysis for cardiovascular applications, Int. J. Numer. Methods Biomed. Eng., 32, e02755, 10.1002/cnm.2755

Edelman, 2001, Degeneracy and complexity in biological systems, Proc. Natl. Acad. Sci. U.S.A., 98, 13763, 10.1073/pnas.231499798

Einevoll, 2009, Sharing with Python, Front. Neurosci., 3, 334, 10.3389/neuro.01.037.2009

Feinberg, 2015, Chaospy: an open source tool for designing methods of uncertainty quantification, J. Comput. Sci., 11, 46, 10.1016/j.jocs.2015.08.008

Ferson, 1996, Different methods are needed to propagate ignorance and variability, Reliabil. Eng. Syst. Saf., 54, 133, 10.1016/S0951-8320(96)00071-3

Ferson, 2004, Summary from the epistemic uncertainty workshop: Consensus amid diversity, Reliab. Eng. Syst. Saf., 85, 355, 10.1016/j.ress.2004.03.023

Garcia, 2014, Neo: an object model for handling electrophysiology data in multiple formats, Front. Neuroinformatics, 8, 10, 10.3389/fninf.2014.00010

Glen, 2012, Estimating Sobol sensitivity indices using correlations, Environ. Model. Softw., 37, 157, 10.1016/j.envsoft.2012.03.014

Goldman, 2001, Global structure, robustness, and modulation of neuronal models, J. Neurosci., 21, 5229, 10.1523/JNEUROSCI.21-14-05229.2001

Golowasch, 2002, Failure of averaging in the construction of a conductance-based neuron model, J. Neurophysiol., 87, 1129, 10.1152/jn.00412.2001

Gutenkunst, 2007, Universally sloppy parameter sensitivities in systems biology models, PLoS Comput. Biol., 3, 1871, 10.1371/journal.pcbi.0030189

Halnes, 2011, A multi-compartment model for interneurons in the dorsal lateral geniculate nucleus, PLoS Comput. Biol., 7, e1002160, 10.1371/journal.pcbi.1002160

Halnes, 2007, Density dependent neurodynamics, Biosystems, 89, 126, 10.1016/j.biosystems.2006.06.010

Halnes, 2009, Modelling and sensitivity analysis of the reactions involving receptor, G-protein and effector in vertebrate olfactory receptor neurons, J. Comput. Neurosci., 27, 471, 10.1007/s10827-009-0162-6

Hamby, 1994, A review of techniques for parameter sensitivity analysis of environmental models, Environ. Monit. Assess., 32, 135, 10.1007/BF00547132

Hammersley, 1960, Monte carlo methods for solving multivariable problems, Ann. N. Y. Acad. Sci., 86, 844, 10.1111/j.1749-6632.1960.tb42846.x

Hay, 2013, Preserving axosomatic spiking features despite diverse dendritic morphology, J. Neurophysiol., 109, 2972, 10.1152/jn.00048.2013

Herman, 2017, SALib: an open-source python library for sensitivity analysis, J. Open Source Softw., 2, 97, 10.21105/joss.00097

Hines, 1997, The NEURON Simulation Environment, Neural Comput., 9, 1179, 10.1162/neco.1997.9.6.1179

Hodgkin, 1952, A quantitative description of membrane current and its application to conduction and excitation in nerve, J. Physiol., 117, 500, 10.1113/jphysiol.1952.sp004764

Homma, 1996, Importance measures in global sensitivity analysis of nonlinear models, Reliabil. Eng. Syst. Saf., 52, 1, 10.1016/0951-8320(96)00002-6

Hora, 1996, Aleatory and epistemic uncertainty in probability elicitation with an example from hazardous waste management, Reliabil. Eng. Syst. Saf., 54, 217, 10.1016/S0951-8320(96)00077-4

Hosder, 2007, Efficient sampling for non-intrusive polynomial chaos applications with multiple uncertain input variables, 48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference, 10.2514/6.2007-1939

Izhikevich, 2003, Simple model of spiking neurons, IEEE Trans. Neural Netw, 14, 1569, 10.1109/TNN.2003.820440

Izhikevich, 2008, Large-scale model of mammalian thalamocortical systems, Proc. Natl. Acad. Sci. U.S.A., 105, 3593, 10.1073/pnas.0712231105

Kiureghian, 2009, Aleatory or epistemic? Does it matter?, Struct. Saf., 31, 105, 10.1016/j.strusafe.2008.06.020

Koch, 1998, Methods in Neuronal Modeling: From Ions to Networks, 2nd Edn

Kuchibhotla, 2017, Parallel processing by cortical inhibition enables context-dependent behavior, Nat. Neurosci., 20, 62, 10.1038/nn.4436

Leamer, 1985, Sensitivity analyses would help, Am. Econ. Rev., 75, 308

Lemieux, 2009, Monte Carlo and Quasi-Monte Carlo Sampling. Springer Series in Statistics

Marder, 2006, Variability, compensation and homeostasis in neuron and network function, Nat. Rev. Neurosci., 7, 563, 10.1038/nrn1949

Marder, 2011, Multiple models to capture the variability in biological neurons and networks, Nat. Neurosci., 14, 133, 10.1038/nn.2735

Marino, 2008, A methodology for performing global uncertainty and sensitivity analysis in systems biology, J. Theor. Biol., 254, 178, 10.1016/j.jtbi.2008.04.011

Markram, 2015, Reconstruction and simulation of neocortical microcircuitry, Cell, 163, 456, 10.1016/j.cell.2015.09.029

Marx, 2009, Ab initio Molecular Dynamics: Basic Theory and Advanced Method, 10.1017/CBO9780511609633

McKerns, 2012, Building a framework for predictive science, CoRR, 1

Merolla, 2014, A million spiking-neuron integrated circuit with a scalable communication network and interface, Science, 345, 668, 10.1126/science.1254642

Morris, 1991, Factorial sampling plans for preliminary computational experiments, Technometrics, 33, 161, 10.1080/00401706.1991.10484804

Muller, 2015, Python in neuroscience, Front. Neuroinformatics, 9, 11, 10.3389/fninf.2015.00011

Mullins, 2016, Separation of aleatory and epistemic uncertainty in probabilistic model validation, Reliabil. Eng. Syst. Saf., 147, 49, 10.1016/j.ress.2015.10.003

Najm, 2009, Uncertainty quantification and polynomial chaos techniques in computational fluid dynamics, Annu. Rev. Fluid Mech., 41, 35, 10.1146/annurev.fluid.010908.165248

Narayan, 2014, Adaptive Leja sparse grid constructions for stochastic collocation and high-dimensional approximation, SIAM J. Sci. Comput., 36, A2952, 10.1137/140966368

Elephant - electrophysiology analysis toolkit2017

Oberkampf, 2002, Error and uncertainty in modeling and simulation, Reliabil. Eng. Syst. Saf., 75, 333, 10.1016/S0951-8320(01)00120-X

O'Donnell, 2017, Beyond excitation/inhibition imbalance in multidimensional models of neural circuit changes in brain disorders, eLife, 6, e26724, 10.7554/eLife.26724

Oliphant, 2007, Python for scientific computing, Comput. Sci. Eng., 9, 10, 10.1109/MCSE.2007.58

Peyser, 2017, Nest 2.14.0

Prinz, 2004, Similar network activity from disparate circuit parameters, Nat. Neurosci., 7, 1345, 10.1038/nn1352

Rifkin, 2007, Notes on Regularized Least Squares

Rosenblatt, 1952, Remarks on a Multivariate Transformation, Ann. Math. Stat., 23, 470, 10.1214/aoms/1177729394

Rossa, 2011, The COST 731 Action: a review on uncertainty propagation in advanced hydro-meteorological forecast systems, Atmos. Res., 100, 150, 10.1016/j.atmosres.2010.11.016

Saltelli, , Making best use of model valuations to compute sensitivity indices, Comput. Phys. Commun., 145, 280, 10.1016/S0010-4655(02)00280-1

Saltelli, , Sensitivity analysis for importance assessment, Risk Anal., 22, 579, 10.1111/0272-4332.00040

Saltelli, 2010, Variance based sensitivity analysis of model output. Design and estimator for the total sensitivity index, Comput. Phys. Commun., 181, 259, 10.1016/j.cpc.2009.09.018

Saltelli, 2007, Global Sensitivity Analysis. The Primer, 10.1002/9780470725184

Schulz, 2007, Quantitative expression profiling of identified neurons reveals cell-specific constraints on highly variable levels of gene expression, Proc. Natl. Acad. Sci. U.S.A., 104, 13187, 10.1073/pnas.0705827104

Sharp, 2003, Qmu and nuclear weapons certification: What's under the hood?, Los Alamos Sci., 28, 47

Smolyak, 1963, Quadrature and interpolation formulas for tensor products of certain classes of functions, Dokl. Akad. Nauk SSSR, 148, 1042

Snowden, 2017, Methods of model reduction for large-scale biological systems: a survey of current methods and trends, Bull. Math. Biol., 79, 1449, 10.1007/s11538-017-0277-2

Sobol, 1967, On the distribution of points in a cube and the approximate evaluation of integrals, USSR Comput. Math. Math. Phys., 7, 86, 10.1016/0041-5553(67)90144-9

Sobol, 1990, Sensitivity analysis for nonlinear mathematical models, Matematicheskoe Modelirovanie, 2, 112

Sterratt, 2011, Principles of Computational Modelling in Neuroscience, 10.1017/CBO9780511975899

Stieltjes, 1884, Quelques recherches sur la théorie des quadratures dites mécaniques, Ann. Sci. 'École Normale Supérieure, 1, 409, 10.24033/asens.245

Sudret, 2008, Global sensitivity analysis using polynomial chaos expansions, Reliab. Eng. Syst. Saf., 93, 964, 10.1016/j.ress.2007.04.002

Taylor, 2009, How multiple conductances determine electrophysiological properties in a multicompartment model, J. Neurosci., 29, 5573, 10.1523/JNEUROSCI.4438-08.2009

Tobin, 2006, Endogenous and half-center bursting in morphologically inspired models of leech heart interneurons, J. Neurophysiol., 96, 2089, 10.1152/jn.00025.2006

Torres Valderrama, 2015, Uncertainty propagation in nerve impulses through the action potential mechanism, J. Math. Neurosci., 5, 3, 10.1186/2190-8567-5-3

Turanyi, 1990, Sensitivity analysis of comprex kinetic systems. Tools and applications, J. Math. Chem., 5, 203, 10.1007/BF01166355

Van Geit, 2008, Automated neuron model optimization techniques: A review, Biol. Cybern., 99, 241, 10.1007/s00422-008-0257-6

Wang, 2015, Combustion kinetic model uncertainty quantification, propagation and minimization, Prog. Energy Combust. Sci., 47, 1, 10.1016/j.pecs.2014.10.002

Xiu, 2010, Numerical Methods for Stochastic Computations: A Spectral Method Approach, 10.2307/j.ctv7h0skv

Xiu, 2005, High-order collocation methods for differential equations with random inputs, SIAM J. Sci. Comput., 27, 1118, 10.1137/040615201

Yildirim, 2015, Stochastic simulations of ocean waves: an uncertainty quantification study, Ocean Model., 86, 15, 10.1016/j.ocemod.2014.12.001

Zhu, 1999, Burst firing in identified rat geniculate interneurons, Neuroscience, 91, 1445, 10.1016/S0306-4522(98)00665-4

Zi, 2011, Sensitivity analysis approaches applied to systems biology models, IET Syst. Biol., 5, 336, 10.1049/iet-syb.2011.0015