Neurofitter: A parameter tuning package for a wide range of electrophysiological neuron models

Frontiers in Neuroinformatics

Volumn 1, Issue NOV, 2007, Pages

  van Geit, Werner ^a,b   Achard, Pablo ^a,c   De Schutter, Erik ^a,b  

^a UNIVERSITY OF ANTWERP   (Belgium)

^b OKINAWA INSTITUTE OF SCIENCE AND TECHNOLOGY GRADUATE UNIVERSITY   (Japan)

^c BRANDEIS UNIVERSITY   (United States)

Author keywords

Automatic; Electrophysiology; Model; Neuron; Optimization algorithms; Parameter tuning; Simulator; Software

Indexed keywords

EID: 56449108218     PISSN: 16625196     EISSN: None     Source Type: Journal    
DOI: 10.3389/neuro.11/001.2007     Document Type: Article

References (65)

1. Abramowitz, M., and Stegun, I. A. (1972). Handbook of mathematical functions with formulas, graphs, and mathematical tables. (New York, Dover).
2. Achard, P., and De Schutter, E. (2006). Complex parameter landscape for a complex neuron model. PLoS Comput. Biol. 2, e94.
3. Ascoli, G. (2006). The ups and downs of neuroscience shares. Neuroinformatics 4, 213-215.
4. Audet, C., and Orban, D. (2004). Finding optimal algorithmic parameters using a mesh adaptive direct search. Cahiers du GERAD G-2004-xx.
5. Baldi, P., Vanier, M. C., and Bower, J. M. (1998). On the use of Bayesian methods for evaluating compartmental neural models. J. Comput. Neurosci. 5, 285-314.
6. Banga, J. R., Moles, C. G., and Alonso, A. A. (2003). Global optimization of bioprocesses using stochastic and hybrid methods. In Frontiers in Global Optimization (Nonconvex Optimization and Its Applications), C. A. Floudas, and P. M. Pardalos, eds. (Kluwer Academic Publishers).
7. Bean, B. P. (2007). The action potential in mammalian central neurons. Nat. Rev. Neurosci. 8, 451-465.
8. Bhalla, U. S., and Bower, J. M. (1993). Exploring parameter space in detailed single neuron models: simulations of the mitral and granule cells of the olfactory bulb. J. Neurophysiol. 69, 1948-1965.
9. Bjaalie, J. G., and Grillner, S. (2007). Global neuroinformatics: the international neuroinformatics coordinating facility. J. Neurosci. 27, 3613-3615.
10. Blanche, T. J., and Swindale, N. V. (2006). Nyquist interpolation improves neuron yield in multiunit recordings. J. Neurosci. Methods 155, 81-91.
11. Bleeck, S., Patterson, R. D., and Winter, I. M. (2003). Using genetic algorithms to find the most effective stimulus for sensory neurons. J. Neurosci. Methods 125, 73-82.
12. Bower, J. M., and Beeman, D. (1998). The book of GENESIS: Exploring realistic neural models with the GEneral NEural SImulation System, 2nd ed. (New York, Springer- Verlag).
13. Brown, E. N., Kass, R. E., and Mitra, P. P. (2004). Multiple neural spike train data analysis: state-of-the-art and future challenges. Nat. Neurosci. 7, 456-461.
14. Bush, K., Knight, J., and Anderson, C. (2005). Optimizing conductance parameters of cortical neural models via electrotonic partitions. Neural Netw. 18, 488-496.
15. Butera, R. J., Rinzel, J., and Smith, J. C. (1999). Models of respiratory rhythm generation in the pre-Bö tzinger complex. I. Bursting pacemaker neurons. J. Neurophysiol. 82, 382-397.
16. Davison, A. P., Feng, J., and Brown, D. (2000). A reduced compartmental model of the mitral cell for use in network models of the olfactory bulb. Brain Res. Bull. 51, 393-399.
17. De Schutter, E., and Beeman, D. (1998). Speeding up GENESIS simulations. In The Book of GENESIS Exploring Realistic Neural Models with the GEneral NEural SImulation System. (Berlin, Springer).
18. De Schutter, E., and Bower, J. M. (1994). An active membrane model of the cerebellar Purkinje cell. I. Simulation of current clamps in slice. J. Neurophysiol. 71, 375-400.
19. De Schutter, E., and Steuber, V. (2000). Modeling of neurons with active dendrites. In Computational Neuroscience: Realistic Modeling for Experimentalists, E. De Schutter, eds. (Boca Raton, CRC Press).
20. De Schutter, E., Ekeberg, O., Kotaleski, J. H., Achard, P., and Lansner, A. (2005). Biophysically detailed modelling of microcircuits and beyond. Trends Neurosci. 28, 562-569.
21. Druckmann, S., Banitt, Y., Gidon, A., Schuermann, F., Markram, H., Segev, I. (2007). A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Front. Neurosci. 1.
22. Eiben, A. E., and Smith, J. E. (2003). Introduction to evolutionary computing (SpringerVerlag).
23. Fellous, J. M., Houweling, A. R., Modi, R. H., Rao, R. P., Tiesinga, P. H., and Sejnowski, T. J. (2001). Frequency dependence of spike timing reliability in cortical pyramidal cells and interneurons. J. Neurophysiol. 85, 1782-1787.
24. Forrest, S., and Mitchell, M. (1993). What makes a problem hard for a genetic algorithm? Some anomalous results and their explanation. Mach. Learn. 13, 285-319.
25. Foster, W. R., Ungar, L. H., and Schwaber, J. S. (1993). Significance of conductances in Hodgkin-Huxley models. J. Neurophysiol. 70, 2502-2518.
26. Gerken, W. C., Purvis, L. K., and Butera, R. J. (2006). Genetic algorithm for optimization and specification of a neuron model. Neurocomputing 69, 1039-1042.
27. Gold, C., Henze, D. A., Koch, C., and Buzsá ki, G. (2006). On the origin of the extracellular action potential waveform: A modeling study. J. Neurophysiol. 95, 3113-3128.
28. Goldman, M. S., Golowasch, J., Marder, E., and Abbott, L. F. (2001). Global structure, robustness, and modulation of neuronal models. J. Neurosci. 21, 5229-5238.
29. Golowasch, J., Goldman, M. S., Abbott, L. F., and Marder, E. (2002). Failure of averaging in the construction of a conductance-based neuron model. J. Neurophysiol. 87, 1129-1131.
30. Hines, M. L., and Carnevale, N. T. (1997). The NEURON simulation environment. Neural Comput. 9, 1179-1209.
31. Hines, M. L., Morse, T., Migliore, M., Carnevale, N. T., and Shepherd, G. M. (2004). ModelDB: A database to support computational neuroscience. J. Comput. Neurosci. 17, 7-11.
32. Holland, J. H. (1975). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence (Ann Arbor, University of Michigan Press).
33. Huys, Q. J., Ahrens, M. B., and Paninski, L. (2006). Efficient estimation of detailed single-neuron models. J. Neurophysiol. 96, 872-890.
34. Jenerick, H. (1963). Phase plane trajectories of the muscle spike potential. Biophys. J. 3, 363-377.
35. Keijzer, M., Merelo, J. J., Romero, G., and Schoenauer, M. (2001). Evolving objects: A general purpose evolutionary computation library. In Artificial Evolution: 5th International Conference, Evolution Artificielle, EA 2001, Le Creusot. (France; Berlin, Springer-Verlag).
36. Kennedy, D. (2006). Where's the beef? missing data in the information age. Neuroinformatics 4, 271-273.
37. Kennedy, J., and Eberhart, R. C. (1995). Particle swarm optimization. Proceedings of the IEEE International Joint Conference on Neural Networks, 1942-1948.
38. Keren, N., Peled, N., and Korngreen, A. (2005). Constraining compartmental models using multiple voltage recordings and genetic algorithms. J. Neurophysiol. 94, 3730-3742.
39. Le Masson, and Maex (2001). Introduction to equation solving and parameter fitting. In Computational Neuroscience: Realistic Modeling for Experimentalists. (London, CRC Press).
40. Lewicki, M. S. (1998). A review of methods for spike sorting: the detection and classification of neural action potentials. Network 9, R53-R78.
41. London, M., and Häusser, M. (2005). Dendritic computation. Annu. Rev. Neurosci. 28, 503-532.
42. Mainen, Z. F., and Sejnowski, T. J. (1995). Reliability of spike timing in neocortical neurons. Science 268, 1503-1506.
43. Mainen, Z. F., and Sejnowski, T. J. (1996). Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382, 363-366.
44. Marder, E., and Goaillard, J. M. (2006). Variability, compensation and homeostasis in neuron and network function. Nat. Rev. Neurosci. 7, 563-574.
45. Markram, H. (2006). The blue brain project. Nat. Rev. Neurosci. 7, 153-160.
46. Migliore, M., Cannia, C., Lytton, W. W., Markram, H., and Hines, M. L. (2006). Parallel network simulations with NEURON. J. Comput. Neurosci. 21, 119-129.
47. Moon, B. R. (1996). Sampling rates, aliasing, and the analysis of electrophysiological signals. Biomedical Engineering Conference, 1996, Proceedings of the 1996 Fifteenth Southern Biomedical Engineering Conference, 1996., Proceedings of the 1996 Fifteenth Southern Sampling rates, aliasing, and the analysis of electrophysiological signals, 401-404.
48. Nagle, D. (2005). MPI - The Complete Reference, Vol. 1: The MPI Core, 2nd ed., Scientific and Engineering Computation Series by Marc Snir, Steve Otto, Steven Huss-Lederman, David Walker, and Jack Dongarra. Sci. Program. 13, 57-63.
49. Paulin, M. G. (1992). Digital filters for firing rate estimation. Biol. Cybern. 66, 525-531.
50. 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. J. Neurophysiol. 90, 3998-4015.
51. Rall, W. (1964). Theoretical significance of dendritic trees for neuronal input-output relations. In Neural Theory and Modeling, R. F. Reiss, eds. (Stanford, California, Stanford University Press).
52. Rall, W., Burke, R. E., Holmes, W. R., Jack, J. J., Redman, S. J., and Segev, I. (1992). Matching dendritic neuron models to experimental data. Physiol. Rev. 72, S159-S186.
53. Rechenberg, I. (1973). Evolutionsstrategie: optimierung technischer systeme nach prinzipien der biologischen evolution. (Stuttgart, Germany, Frommann-Holzboog).
54. Reid, M. S., Brown, E. A., and DeWeerth, S. P. (2007). A parameter-space search algorithm tested on a Hodgkin-Huxley model. Biol. Cybern. 96, 625-634.
55. Schneider, S., Igel, C., Klaes, C., Dinse, H. R., and Wiemer, J. C. (2004). Evolutionary adaptation of nonlinear dynamical systems in computational neuroscience. Genet. Programming Evolvable Mach. 5, 215-227.
56. Schwefel, H. P. (1975). Evolutionsstrategie und numerische optimierung. PhD Thesis.
57. Shadlen, M. N., and Newsome, W. T. (1998). The variable discharge of cortical neurons: implications for connectivity, computation, and information coding. J. Neurosci. 18, 3870-3896.
58. Shen, G. Y., Chen, W. R., Midtgaard, J., Shepherd, G. M., and Hines, M. L. (1999). Computational analysis of action potential initiation in mitral cell soma and dendrites based on dual patch recordings. J. Neurophysiol. 82, 3006-3020.
59. Smith, J. C., Ellenberger, H. H., Ballanyi, K., Richter, D. W., and Feldman, J. L. (1991). Pre-Bö tzinger complex: a brainstem region that may generate respiratory rhythm in mammals. Science 254, 726-729.
60. Stiefel, K. M., and Sejnowski, T. J. (2007). Mapping function onto neuronal morphology. J. Neurophysiol. 98, 513-526.
61. Taylor, A. L., Hickey, T. J., Prinz, A. A., and Marder, E. (2006). Structure and visualization of high-dimensional conductance spaces. J. Neurophysiol. 96, 891-905.
62. Traub, R. D., Wong, R. K., Miles, R., and Michelson, H. (1991). Amodel of a CA3 hippocampal pyramidal neuron incorporating voltage-clamp data on intrinsic conductances. J. Neurophysiol. 66, 635-650.
63. Vanier, M. C., and Bower, J. M. (1999). A comparative survey of automated parametersearch methods for compartmental neural models. J. Comput. Neurosci. 7, 149-171.
64. Weaver, C. M., and Wearne, S. L. (2006). The role of action potential shape and parameter constraints in optimization of compartment models. Neurocomputing 69, 1053-1057.
65. Wolpert, D. H., and Macready, W. G. (1997). No free lunch theorems for optimization. IEEE Trans. Evol. Comput. 1, 67-82.