Efficient fitting of conductance-based model neurons from somatic current clamp

Journal of Computational Neuroscience

Volumn 32, Issue 1, 2012, Pages 1-24

  Lepora, Nathan F ^a   Overton, Paul G ^a   Gurney, Kevin ^a  

^a UNIVERSITY OF SHEFFIELD   (United Kingdom)

Author keywords

Conductance based model neuron; Fast spiking neuron; Genesis; Hodgkin Huxley model; Minimal model; Model fitting; Neocortex; Neuronal homeostasis; Optimization; Pyramidal neuron; Sensitivity

Indexed keywords

ELECTROPHYSIOLOGY;

CONDUCTANCE BASED MODELING; CONDUCTANCE-BASED MODEL NEURON; FAST SPIKING; FAST SPIKING NEURON; GENESIS; HODGKIN-HUXLEY MODELS; HOMOEOSTASIS; MINIMAL MODEL; MODEL FITTING; NEOCORTEX; NEURONAL HOMEOSTASIS; OPTIMISATIONS; PYRAMIDAL NEURON; SENSITIVITY; SPIKING NEURONES;

NEURONS;

ANALYTICAL ERROR; ARTICLE; BRAIN CORTEX; CELL FUNCTION; CLAMP; COMPARTMENT MODEL; CONTROLLED STUDY; DENDRITE; ELECTRIC CURRENT; HODGKIN HUXLEY EQUATION; KINETICS; MATHEMATICAL COMPUTING; MEMBRANE BIOLOGY; MOLECULAR DYNAMICS; NERVE CELL MEMBRANE CONDUCTANCE; NERVE CELL MEMBRANE POTENTIAL; NERVE CELL NETWORK; NERVOUS SYSTEM ELECTROPHYSIOLOGY; PRIORITY JOURNAL; SAMPLING; THALAMUS; VALIDATION PROCESS;

EID: 84858794369     PISSN: 09295313     EISSN: 15736873     Source Type: Journal    
DOI: 10.1007/s10827-011-0331-2     Document Type: Article

References (58)

1. Achard, P., & De Schutter, E. (2006). Complex parameter landscape for a complex neuron model. PLoS Computational Biology, 2(7), e94.
2. Achard, P., & De Schutter, E. (2008). Calcium, synaptic plasticity and intrinsic homeostasis in Purkinje neuron models. Frontiers in Computational Neuroscience, 2, 8.
3. Booth, V., & Rinzel, J. (1995). A minimal, compartmental model for a dendritic origin of bistability of motoneuron firing patterns. Journal of Computational Neuroscience, 2(4), 299-312. (Pubitemid 3008700)
4. Bower, J. M., & Beeman, D. (1998). The book of GENESIS. New York: Springer.
5. Bush, K., Knight, J., & Anderson, C. (2005). Optimizing conductance parameters of cortical neural models via electrotonic partitions. Neural Networks, 18(5-6), 488-496. (Pubitemid 43186567)
6. Connors, B. W., & Gutnick, M. J. (1990). Intrinsic firing patterns of diverse neocortical neurons. Trends in Neurosciences, 13(3), 99-104. (Pubitemid 20062535)
7. Davis, G. W. (2006).Homeostatic control of neural activity: From phenomenology tomolecular design. Annual Review of Neuroscience, 29, 307-323.
8. Druckmann, S., Banitt, Y., Gidon, A., Schurmann, F., Markram, H., & Segev, I. (2007). A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Frontiers in Neuroscience, 1(1), 7.
9. Druckmann, S., Berger, T. K., Hill, S., Schurmann, F., Markram, H.,&Segev, I. (2008). Evaluating automated parameter constraining procedures of neuron models by experimental and surrogate data. Biological Cybernetics, 99(4), 371-379.
10. Edelman, G. M., & Gally, J. A. (2001). Degeneracy and complexity in biological systems. Proceedings of the National Academy of Sciences of the United States of America, 98(24), 13763.
11. Gerstner, W., & Naud, R. (2009). Neuroscience. how good are neuron models? Science, 326(5951), 379-80.
12. Goldman, M. S., Golowasch, J., Marder, E., & Abbott, L. F. (2001). Global structure, robustness, and modulation of neuronal models. Journal of Neuroscience, 21(14), 5229.
13. Golowasch, J., Goldman, M. S., Abbott, L. F., & Marder, E. (2002). Failure of averaging in the construction of a conductance-based neuron model. Journal of Neurophysiology, 87(2), 1129.
14. Gunay, C., Edgerton, J. R., & Jaeger, D. (2008). Channel density distributions explain spiking variability in the globus pallidus: A combined physiology and computer simulation database approach. Journal of Neuroscience, 28(30), 7476.
15. Herz, A. V. M., Gollisch, T., Machens, C. K., & Jaeger, D. (2006).Modeling single-neuron dynamics and computations: A balance of detail and abstraction. Science, 314(5796), 80-85. (Pubitemid 44547744)
16. Hines, M. (1984). Efficient computation of branched nerve equations. International Journal of Bio-Medical Computing, 15(1), 69-76.
17. Hobbs, K. H., & Hooper, S. L. (2008). Using complicated, wide dynamic range driving to develop models of single neurons in single recording sessions. Journal of Neurophysiology, 99(4), 1871.
18. Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology (London), 117, 500-544.
19. Huys, Q. J. M., Ahrens, M. B., & Paninski, L. (2006). Efficient estimation of detailed single-neuron models. Journal of Neurophysiology, 96(2), 872-890. (Pubitemid 44159755)
20. Huys, Q. J. M., & Paninski, L. (2009). Smoothing of, and parameter estimation from, noisy biophysical recordings. PLoS Computational Biology, 5(5), e1000379.
21. Izhikevich, E.M. (2003). Simple model of spiking neurons. IEEE Transactions on Neural Networks, 14(6), 1569-1572.
22. Keren, N., Peled, N., & Korngreen, A. (2005). Constraining compartmental models using multiple voltage recordings and genetic algorithms. Journal of neurophysiology, 94(6), 3730.
23. Kita, T., Kita, H., & Kita, S. T. (1984). Passive electrical membrane properties of rat neostriatal neurons in an in vitro slice preparation. Brain Research, 300, 129-139. (Pubitemid 14104100)
24. Lander, E. S., Linton, L. M., Birren, B., Nusbaum, C., Zody, M. C., Baldwin, J., et al. (2001). Initial sequencing and analysis of the human genome. Nature, 409(6822), 860-921.
25. LeMasson, G., & Maex, R. (2001). Introduction to equation solving and parameter fitting. In Computational neuroscience: Realistic modelling for experimentalists (pp. 1-25).
26. Lepora, N. F., Overton, P., & Gurney, K. (2009). Efficient current-based optimization techniques for parameter estimation in multi-compartment neuronal models. BMC Neuroscience, 10(Suppl 1), 347.
27. Lindau, M.,&Neher, E. (1988). Patch-clamp techniques for time-resolved capacitance measurements in single cells. Pf lügers Archiv European Journal of Physiology, 411(2), 137-146. (Pubitemid 18066957)
28. Llano, I., Marty, A., Armstrong, C. M., & Konnerth, A. (1991). Synaptic-and agonist-induced excitatory currents of Purkinje cells in rat cerebellar slices. The Journal of Physiology, 434(1), 183.
29. London, M., & Hausser, M. (2005). Dendritic computation. Annual Review of Neuroscience, 28(1), 503-532. (Pubitemid 41098913)
30. Mainen, Z. F., & Sejnowski, T. J. (1996). Influence of dendritic structure on firing pattern in model neocortical neurons. Nature, 382(6589), 363-366. (Pubitemid 26260465)
31. Marder, E., & Goaillard, J. M. (2006). Variability, compensation and homeostasis in neuron and network function. Nature Reviews Neuroscience, 7(7), 563-574. (Pubitemid 43995432)
32. Marder, E., & Prinz, A. A. (2002). Modeling stability in neuron and network function: The role of activity in homeostasis. Bioessays, 24(12), 1145-1154. (Pubitemid 35464944)
33. Markram, H. (2006). The blue brain project. Nature Reviews Neuroscience, 7(2), 2153-2160.
34. Mennerick, S., Que, J., Benz, A., & Zorumski, C. F. (1995). Passive and synaptic properties of hippocampal neurons grown in microcultures and in mass cultures. Journal of neurophysiology, 73(1), 320.
35. Moore, E. H. (1920). On the reciprocal of the general algebraic matrix. Bulletin of the American Mathematical Society, 26, 394-395.
36. Morse, T. M., Davison, A. P., & Hines, M. (2001). Parameter space reduction in neuron model optimization through minimization of residual voltage clamp current. In 31st Annual Meeting of the Society for Neuroscience, San Diego, CA, USA, 10-15 November 2001. Society for Neuroscience Abstracts (Vol. 27).
37. Penrose, R. (1955). A generalized inverse for matrices. Proceedings of the Cambridge Philosophical Society, 51, 406-413.
38. Pinsky, P. F., & Rinzel, J. (1994). Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons. Journal of Computational Neuroscience, 1(1), 39-60.
39. Pospischil, M., Toledo-Rodriguez, M., Monier, C., Piwkowska, Z., Bal, T., Frégnac, Y., et al. (2008). Minimal Hodgkin- Huxley type models for different classes of cortical and thalamic neurons. Biological Cybernetics, 99(4), 427-441.
40. Prinz, A. A., Billimoria, C. P., & Marder, E. (2003). Alternative to hand-tuning conductance-based models: Construction and analysis of databases of model neurons. Journal of Neurophysiology, 90(6), 3998.
41. Rall, W. (1960). Membrane potential transients and membrane time constant of motoneurons. Experimental Neurology, 2, 503-532.
42. Rall, W. (1969). Time constants and electrotonic length of membrane cylinders and neurons. Biophysics Journal, 9(12), 1483-1508.
43. Schulz, D. J., Goaillard, J. M., & Marder, E. (2006). Variable channel expression in identified single and electrically coupled neurons in different animals. Nature Neuroscience, 9(3), 356-362. (Pubitemid 43290965)
44. Swensen, A. M., & Bean, B. P. (2005). Robustness of burst firing in dissociated purkinje neurons with acute or long-term reductions in sodium conductance. Journal of Neuroscience, 25(14), 3509.
45. Tabak, J., Murphey, C. R., & Moore, L. E. (2000). Parameter estimation methods for single neuron models. Journal of Computational Neuroscience, 9(3), 215-236. (Pubitemid 32000321)
46. Taylor, A. L., Goaillard, J. M., & Marder, E. (2009). How multiple conductances determine electrophysiological properties in a multicompartment model. Journal of Neuroscience, 29(17), 5573.
47. Taylor, A. L., Hickey, T. J., Prinz, A. A., & Marder, E. (2006). Structure and visualization of high-dimensional conductance spaces. Journal of Neurophysiology, 96(2), 891.
48. Tien, J. H., & Guckenheimer, J. (2008). Parameter estimation for bursting neural models. Journal of Computational Neuroscience, 24(3), 358-373.
49. Tobin, A. E., & Calabrese, R. L. (2006). Endogenous and half-center bursting in morphologically inspired models of leech heart interneurons. Journal of Neurophysiology, 96(4), 2089.
50. Turrigiano, G. G., & Nelson, S. B. (2004). Homeostatic plasticity in the developing nervous system. Nature Reviews Neuroscience, 5(2), 97-107. (Pubitemid 38160285)
51. Van Geit, W., Achard, P., & De Schutter, E. (2007). Neurofitter: Aparameter tuning package for a wide range of electrophysiological neuron models. Frontiers in Neuroinformatics, 1, 1.
52. Van Geit, W., De Schutter, E., & Achard, P. (2008). Automated neuron model optimization techniques: A review. Biological Cybernetics, 99(4), 241-251.
53. Vanier, M. C., & Bower, J. M. (1999). A comparative survey of automated parameter-search methods for compartmental neural models. Journal of Computational Neuroscience, 7(2), 149-171. (Pubitemid 29433535)
54. Venter, J. C., Adams, M. D., Myers, E. W., Li, P. W., Mural, R. J., Sutton, G. G., et al. (2001). The sequence of the human genome. Science, 291(5507), 1304.
55. Weaver, C. M., &Wearne, S. L. (2006). The role of action potential shape and parameter constraints in optimization of compartment models. Neurocomputing, 69(10-12), 1053-1057.
56. Weaver, C. M., & Wearne, S. L. (2008). Neuronal firing sensitivity to morphologic and active membrane parameters. PLoS Computational Biology, 4(1), e11.
57. Wood, R., & Gurney, K. N. (2002). A new parametric search technique for biophysical models. A study in the rat neostriatal projection neuron. In 32nd annual meeting of the Society for Neuroscience, Orlando, FL, USA, 2-7 November 2002. Society for Neuroscience abstracts (Vol. 28).
58. Wood, R., Gurney, K. N., & Wilson, C. J. (2004). A novel parameter optimisation technique for compartmental models applied to a model of a striatal medium spiny neuron. Neurocomputing, 58-60, 1109-1116. (Pubitemid 38684370)