COMPUTATIONAL
NEUROSCIENCE

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Selected papers on modeling methods and tools that may be of general interest to computational neurobiologists.

Bakker, R., Wachtler, T. and Diesmann, M. (2012) CoCoMac 2.0 and the future of tract-tracing databases. Frontiers in Neuroinformatics, Epub 27 Dec. 2012, doi: 10.3389/fninf.2012.00030.

Bedard, C., Kroger, H., and Destexhe, A. (2004) Modeling extracellular field potentials and the frequency-filtering properties of extracellular space. Biophysical Journal, 86, 1829-1842.

Beeman, D., Bower, J.M., De Schutter, E., Efthimiadis, E.N., Goddard, N., & Leigh, J. (1997) The GENESIS simulator-based neuronal database. In: Koslow, S.H. and Huerta, M.F. (Eds.) Neuroinformatics: An Overview of the Human Brain Project. Mahwah NJ: Lawrence Erlbaum Associates, pp. 57-81.

[PDF] Blackwell K.T. and Hellgren Kotaleski J. (2003) Modeling the dynamics of second messenger pathways. In: Kotter, R. (Ed.) Neuroscience Databases: A Practical Guide. Norwell, MA: Kluwer Academic Publishers.

Blundell, I., Brette, R., Cleland, T.A., Close, T.G., Coca, D., Davison, A.P., Diaz-Pier, S., Fernandez Musoles, C., Gleeson, P., Goodman, D.F.M., Hines, M., Hopkins, M.W., Kumbhar, P., Lester, D.R., Marin, B., Morrison, A., Müller, E., Nowotny, T., Peyser, A., Plotnikov, D., Richmond, P., Rowley, A., Rumpe, B., Stimberg, M., Stokes, A.B., Tomkins, A., Trensch, G., Woodman, M. and Eppler, J.M. (2018) Code Generation in Computational Neuroscience: A Review of Tools and Techniques. Frontiers in Neuroinformatics, 05 Nov. 2018, https://doi.org/10.3389/fninf.2018.00068.

Brette, R. (2015) Philosophy of the Spike: Rate-Based vs. Spike-Based Theories of the Brain. Frontiers Systems Neuroscience, 10 Nov. 2015, http://dx.doi.org/10.3389/fnsys.2015.00151.

Cannon, R.C. and D'Alessandro, G. (2007) The Ion Channel Inverse Problem: CurrentNeuroinformatics Meets Biophysics. PLoS Computational Biology, 2(8):e91.

[PDF] Cannon, R.C., Gewaltig, M., Gleeson, P., Bhalla, U., Cornelis, H., Hines, M., Howell, F., Muller, E., Stiles, J., Wils, S., and De Schutter, E. (2007) Interoperability of Neuroscience Modeling Software: Current Status and Future Directions. Neuroinformatics, 5

Carnevale, N.T., Tsai, K.Y., Claiborne, B.J., and Brown, T.H. (1995) The electrotonic transformation: a tool for relating neuronal form to function. In: Tesauro, G., Touretzky, D.S., and Leen, T.K. (Eds.) Advances in Neural Information Processing Systems, Vol. 7, Cambridge, MA: MIT Press, pp. 69-76.

Cornelis, H., Coop, A.D., and Bower, J.M. (2012) A Federated Design for a Neurobiological Simulation Engine: The CBI Federated Software Architecture. PLoS ONE, 7(1): e28956. doi:10.1371/journal.pone.0028956.

Cornelis, H., Rodriguez, A.L., Coop, A.D., Bower, J.M. (2012) Python as a Federation Tool for GENESIS 3.0. PLoS ONE, 7(1): e29018. doi:10.1371/journal.pone.0029018.

[PDF] Cornelis, Hugo and De Schutter, Eric (2003) NeuroSpaces: separating modeling and simulation. Neurocomputing, 52-54, 227-231.

Crook, S., Beeman, D., Gleeson, P., and Howell, F. (2005) XML for Model Specification in Neuroscience: An Introduction and Workshop Summary. Brains, Minds & Media. 1, bmm228 (urn:nbn:de:0009-3-2282).

Davison, A.P., Brüderle, D., Eppler, J., Kremkow, J., Muller, E., Pecevski, D. Perrinet, L. and Yger, P. (2009) PyNN: a common interface for neuronal network simulators. Frontiers in Neuroinformatics, 2:11. doi:10.3389/neuro.11.011.2008.

De Schutter, Eric (1992) A consumer guide to neuronal modeling software. Trends in Neuroscience, 15, 462-464.

Eichler-West, Rogene M., De Schutter, E., & Wilcox, G.L. (1999) Using evolutionary algorithms to search for control parameters in a nonlinear partial differential equation. In Evolutionary Algorithms, Vol. 111 of the IMA Volumes in Mathematics and its Applications, Springer-Verlag, 33-64 (1999).

Eppler, J.M., Helias, M., Muller, E., Diesmann, M. and Gewaltig, M-O. (2009) PyNEST: A convenient interface to the NEST simulator. Frontiers in Neuroinformatics, 2:12. doi: 10.3389/neuro.11.012.2008.

Erdi, Peter. (2014) Teaching Computational Neuroscience. arXiv.org, arXiv:1412.5909 [v1] Thu, 18 Dec 2014.

Galan, R.F., Ermentrout, G.B., and Urban, N.N. (2005) Efficient Estimation of Phase-Resetting Curves in Real Neurons and its Significance for Neural-Network Modeling. Physical Review Letters, 94, 158101.

Gewaltig, M. and Cannon, R.C. (2014) Current Practice in Software Development for Computational Neuroscience and How to Improve It PLOS Computational Biology, 10(1):e1003376. doi: 10.1371/journal.pcbi.1003376. Epub 2014 Jan 23.

Gleeson, P., Steuber, V., and Silver, R.A. (2007) neuroConstruct: A Tool for Modeling Networks of Neurons in 3D Space Neuron, 54, 219-235.

[PDF] Goddard, N., Hucka, M., Howell, F., Cornelis, H., Shankar, K. and Beeman, D. (2001) Towards NeuroML: Model Description Methods for Collaborative Modeling in Neuroscience. Phil. Trans. Royal Society B, 356 (1412), 1209-1228.

Goodman, D.F. and Brette, R. (2008) Brian: a simulator for spiking neural networks in Python. Frontiers in Neuroinformatics, 2:5. doi:10.3389/neuro.11.005.2008.

Graham, B.P., and van Ooyen, A. (2006) Mathematical modelling and numerical simulation of the morphological development of neurons. BMC Neuroscience, 7 (Suppl 1):S9.

Hines, M.L., and Carnevale, N.T. (1997) The NEURON Simulation Environment. Neural Computation, 9 (6), 1179-1209.

[PDF] Hines, M.L., and Carnevale, N.T. (2000) Expanding NEURON's Repertoire of Mechanisms with NMODL. Neural Computation, 12, 839-851.

Grossman, N., Simiaki, V., Martinet, C., Toumazou, C., Schultz, S.R., and Nikolic, K. (2012) The spatial pattern of light determines the kinetics and modulates backpropagation of optogenetic action potentials. Journal of Computational Neuroscience, doi:10.1007/s10827-012-0431-7.

Hines, M., Davison, A.P. and Muller, E. (2009) NEURON and Python. Frontiers in Neuroinformatics, 3:1. doi:10.3389/neuro.11.001.2009.

[PDF] Hodgkin, A.L., and Huxley, A.F. (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 117, 500-544.

Izhikevich, E.M. (2004) Which Model to Use for Cortical Spiking Neurons? IEEE Transactions on Neural Networks, 15, 1063-1070.

Jaeger, D. (2005) Realistic Single Cell Modeling - from Experiment to Simulation Brains, Minds, and Media, 1:bmm222 (urn:nbn:de:0009-3-2228).

[PDF] Kotter, R. (2004) Neuroscience databases: tools for exploring brain structure-function relationships. Philos Trans R Soc Lond B Biol Sci, 356(1412), 1111-1120.

Liu, Z., Golowasch, J., Marder, E., and Abbott, L.F. (1998) A Model Neuron with Activity-Dependent Conductances Regulated by Multiple Calcium Sensors. The Journal of Neuroscience, 18(7), 2309-2320.

[PDF] Natschläger, T., Markram, H., and Maass, W. (2002). Computer models and analysis tools for neural microcircuits. In Kötter, R. (Ed.) Neuroscience Databases: A Practical Guide. Norwell, MA: Kluwer Academic Publishers, pp. 121-136.

Nordlie E, Gewaltig M-O, Plesser HE (2009) Towards reproducible descriptions of neuronal network models. PLoS Computational Biology, 5(8):e1000456.

Parekh R. and Ascoli, A.A. (2013) Neuronal Morphology Goes Digital: A Research Hub for Cellular and System Neuroscience. Neuron, 77(6): 1017–1038. doi: 10.1016/j.neuron.2013.03.008.

[PDF] Prinz, A.A., Abbott, L.F., and Marder, E. (2004) The Dynamic Clamp Comes of Age. Trends in Neuroscience, 27:218-224.

Prinz, A.A., Billimoria, C.P., and Marder, E. (2003) Alternative to Hand-Tuning Conductance-Based Models: Construction and Analysis of Databases of Model Neurons. Journal of Neurophysiology, 90, 3998-4015.

Ray, S. and Bhalla, U.S. (2008) PyMOOSE: interoperable scripting in Python for MOOSE. Frontiers in Neuroinformatics, 2:6. doi:10.3389/neuro.11.006.2008.

Shemer, I., Brinne, B., Tegner, J., Grillner, S. (2008) Electrotonic Signals along Intracellular Membranes May Interconnect Dendritic Spines and Nucleus. PLOS Computational Biology, Mar 28;4(3):e1000036.

Stephan K.E. (2013) The history of CoCoMac. NeuroImage, 80, 46-52. doi: 10.1016/j.neuroimage.2013.03.016.

[PS] Stuart, G.J. and Spruston, N.(2015) Dendritic integration: 60 years of progress. Nature Neuroscience, 18(12):1713-21. doi: 10.1038/nn.4157.

Topalidou, M., Leblois, A., Boraud, T. and Rougier, N.P. (2015) A long journey into reproducible computational neuroscience. Frontiers in Computational Neuroscience, 05 March 2015. http://dx.doi.org/10.3389/fncom.2015.00030.

Vetter, P., Roth, A., and Hausser, M. (2001) Propagation of Action Potentials in Dendrites Depends on Dendritic Morphology. Journal of Neurophysiology, 85, 926-937.

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Last updated Apr. 4, 2022.