Comparison of different neuron models to conductance-based post-stimulus time histograms obtained in cortical pyramidal cells using dynamic-clamp in vitro

Biological Cybernetics

Volumn 105, Issue 2, 2011, Pages 167-180

  Pospischil, Martin ^a   Piwkowska, Zuzanna ^a   Bal, Thierry ^a   Destexhe, Alain ^a  

^a Unite de Neurosciences Integratives et Computationnelles   (France)

Author keywords

Cerebral cortex; Genetic algorithms; In vitro; Model fitting; Spiking neuron models

Indexed keywords

CEREBRAL CORTEX; DATA SETS; HIGH CONDUCTANCE STATE; IN VITRO; INTEGRATE AND FIRES; MODEL FITTING; NETWORK SIMULATION; NEURON MODEL; PYRAMIDAL CELL; SPIKING NEURON MODELS; STIMULATION PROTOCOLS; STIMULUS INTENSITY; SYNAPTIC INTERACTIONS; VISUAL CORTEXES;

GRAPHIC METHODS; LEARNING ALGORITHMS; NEURAL NETWORKS; TELEMETERING; WELL STIMULATION;

COMPUTER SIMULATION;

CAVIA;

ACTION POTENTIAL; ALGORITHM; ANIMAL; ARTICLE; BIOLOGICAL MODEL; BRAIN CORTEX; COMPARATIVE STUDY; COMPUTER SIMULATION; CYBERNETICS; CYTOLOGY; GUINEA PIG; IN VITRO STUDY; NERVE CONDUCTION; PATCH CLAMP; PHYSIOLOGY; PYRAMIDAL NERVE CELL;

ACTION POTENTIALS; ALGORITHMS; ANIMALS; CEREBRAL CORTEX; COMPUTER SIMULATION; CYBERNETICS; GUINEA PIGS; MODELS, NEUROLOGICAL; NEURAL CONDUCTION; PATCH-CLAMP TECHNIQUES; PYRAMIDAL CELLS;

EID: 80755139714     PISSN: 03401200     EISSN: 14320770     Source Type: Journal    
DOI: 10.1007/s00422-011-0458-2     Document Type: Article

References (43)

1. P Achard E De Schutter 2006 Parameter landscape for a complex neuron model PLoS Comput Biol 2 794 804 10.1371/journal.pcbi.0020094 1:CAS:528:DC%2BD28Xns1Chsbo%3D (Pubitemid 44147389)
2. L Badel S Lefort R Brette CH Petersen W Gerstner MJE Richardson 2008 I-V curves are reliable predictors of naturalistic pyramidal-neuron voltage traces J Neurophysiol 99 656 666 18057107 10.1152/jn.01107.2007 (Pubitemid 351253226)
3. R Brette W Gerstner 2005 exponential integrate-and-fire model as an effective description of neuronal activity. J Neurophysiol 94 3637 3642 16014787 10.1152/jn.00686.2005 (Pubitemid 41504797)
4. R Brette Z Piwkowska C Monier M Rudolph-Lilith J Fournier M Levy Y Frégnac T Bal A Destexhe 2008 intracellular recordings using a real-time computational model of the electrode. Neuron 59 379 391 18701064 10.1016/j.neuron.2008.06.021 1:CAS:528:DC%2BD1cXhtVegsbrL
5. C Clopath R Jolivet A Rauch HR Lüscher W Gerstner 2007 Predicting neuronal activity with simple models of the threshold type: Adaptive Exponential Integrate-and-Fire model with two compartments. Neurocomputing 70 1668 1673 10.1016/j.neucom.2006.10.047 (Pubitemid 46656950)
6. A Destexhe 1997 Conductance-based integrate-and-fire models Neural Comput 9 503 514 9097470 10.1162/neco.1997.9.3.503 1:STN:280:DyaK2s3ltFylsw%3D%3D (Pubitemid 127648991)
7. A Destexhe ZF Mainen TJ Sejnowski 1994 An efficient method for computing synaptic conductances based on a kinetic model of receptor binding Neural Comput 6 14 18 10.1162/neco.1994.6.1.14
8. A Destexhe M Rudolph JM Fellous TJ Sejnowski 2001 Fluctuating synaptic conductances recreate in-vivo-like activity in neocortical neurons Neuroscience 107 13 24 11744242 10.1016/S0306-4522(01)00344-X 1:CAS:528:DC%2BD3MXptVSgsb0%3D (Pubitemid 34020681)
9. A Destexhe M Rudolph D Paré 2003 The high-conductance state of neocortical neurons in vivo Nat Rev Neurosci 4 739 751 12951566 10.1038/nrn1198 1:CAS:528:DC%2BD3sXmvVWksbo%3D (Pubitemid 37280153)
10. S Druckmann Y Banitt A Gidon F Schürmann H Markram I Segev 2007 A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data Frontiers Neurosci 1 7 18 10.3389/neuro.01.1.1.001.2007
11. GB Ermentrout 1996 Type 1 membranes, phase resetting curves, and synchrony Neural Comput 8 979 1001 8697231 10.1162/neco.1996.8.5.979 1:STN:280:DyaK283ptFOisA%3D%3D
12. R Fitzhugh 1961 Impulses and physiological states in models of nerve membrane Biophys J 1 445 466 19431309 10.1016/S0006-3495(61)86902-6 1:STN:280:DC%2BD1MrhsFyhtA%3D%3D
13. N Fourcaud-Trocmé D Hansel C van Vreeswijk N Brunel 2003 How spike generation mechanisms determine the neuronal response to fluctuating inputs J Neurosci 23 11628 11640 14684865 (Pubitemid 38021290)
14. JM Goaillard AL Taylor DJ Schulz E Marder 2009 Functional consequences of animal-to-animal variation in circuit parameters Nature Neurosci 12 1424 1430 19838180 10.1038/nn.2404 1:CAS:528:DC%2BD1MXht1OgsrzF
15. A Herrmann W Gerstner 2001 Noise and the PSTH response to current transients: I. General theory and application to the integrate-and-fire neuron J Comput Neurosci 11 135 151 11717530 10.1023/A:1012841516004 1:STN:280:DC%2BD3MnntFGlsA%3D%3D (Pubitemid 34051483)
16. A Herrmann W Gerstner 2002 Noise and the PSTH response to current transients: II. Integrate-and-fire model with slow recovery and application to motoneuron data J Comput Neurosci 12 83 95 12053155 10.1023/A:1015739523224 1:STN:280:DC%2BD38zhtVCisA%3D%3D (Pubitemid 34811906)
17. ML Hines NT Carnevale 1997 The NEURON simulation environment Neural Comput 9 1179 1209 9248061 10.1162/neco.1997.9.6.1179 1:STN:280: DyaK2szptFamtg%3D%3D (Pubitemid 127469144)
18. AL Hodgkin AF Huxley 1952 A quantitative description of membrane current and its application to conduction and excitation in nerve J Physiol 117 500 544 12991237 1:STN:280:DyaG3s%2FhsFGjug%3D%3D
19. EM Izhikevich 2003 Simple model of spiking neurons IEEE Trans Neural Networks 14 1569 1572 10.1109/TNN.2003.820440 1:STN:280:DC%2BD1c%2FoslSmsQ%3D%3D
20. EM Izhikevich 2004 Which model to use for cortical spiking neurons? IEEE Trans Neural Networks 15 1063 1070 10.1109/TNN.2004.832719
21. R Jolivet TJ Lewis W Gerstner 2004 Generalized integrate-and-fire models of neuronal activity approximate spike trains of a detailed model to a high degree of accuracy J Neurophysiol 92 959 976 15277599 10.1152/jn.00190.2004 (Pubitemid 38931960)
22. R Jolivet A Rauch HR Lüscher W Gerstner 2006 Predicting spike timing of neocortical pyramidal neurons by simple threshold models J Comput Neurosci 21 35 49 16633938 10.1007/s10827-006-7074-5 (Pubitemid 44028469)
23. R Jolivet F Schürmann TK Berger R Naud W Gerstner A Roth 2008 The quantitative single-neuron modeling competition Biol Cybern 99 417 426 19011928 10.1007/s00422-008-0261-x
24. R Kobayashi Y Tsubo S Shinomoto 2009 Made-to-order spiking neuron model equipped with a multi-timescale adaptive threshold Frontiers Comput Neurosci 3 9
25. L Lapicque 1907 Recherches quantitatives sur l'excitation electrique des nerfs traitée comme une polarisation J Physiol Pathol Gen 9 620 635
26. PE Latham BJ Richmond PG Nelson S Nirenberg 2000 Intrinsic dynamics in neuronal networks. I. Theory J Neurophysiol 83 808 827 10669496 1:STN:280:DC%2BD3c7jsVKmtg%3D%3D (Pubitemid 30091292)
27. Le Franc Y, Foutry B, Nagy F, Le Masson G. (2001) Nociceptive signal transfer through the dorsal horn network: hybrid and dynamic clamp approaches using a real-time implementation of the Neuron simulation environment. Soc Neurosci Abstracts 27: 927.18
28. Le Masson G Introduction to equation solving and parameter fitting. In: De Schutter E, editor. Computational neuroscience: realistic modeling for experimentalists. CRC Press, London
29. Le Masson G, Maex R. Introduction to equation solving and parameter fitting. In: De Schutter E, editor. Computational neuroscience: realistic modeling for experimentalists. CRC Press, London
30. C Morris H Lecar 1981 Voltage oscillations in the barnacle giant muscle fiber Biophys J 35 193 213 7260316 10.1016/S0006-3495(81)84782-0 1:STN:280:DyaL3M3lslSmtw%3D%3D (Pubitemid 11079105)
31. JS Nagumo S Arimoto S Yoshizawa 1962 An active pulse transmission line simulating nerve axon Proc IRE 50 2061 2070 10.1109/JRPROC.1962.288235
32. Z Piwkowska M Pospischil R Brette J Sliwa M Rudolph-Lilith T Bal A Destexhe 2008 Characterizing synaptic conductance fluctuations in cortical neurons and their influence on spike generation J Neurosci Methods 169 302 322 18187201 10.1016/j.jneumeth.2007.11.010
33. M Pospischil M Toledo-Rodriguez C Monier Z Piwkowska T Bal Y Frégnac H Markram A Destexhe 2008 Minimal Hodgkin-Huxley type models for different classes of cortical and thalamic neurons Biol Cybern 99 427 441 19011929 10.1007/s00422-008-0263-8
34. Press WH, Flannery BP, Teukolsky SA (1992) Numerical Recipes in C: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge
35. HP Robinson N Kawai 1993 Injection of digitally synthesized synaptic conductance transients to measure the integrative properties of neurons J Neurosci Methods 49 157 165 7903728 10.1016/0165-0270(93)90119-C 1:STN:280:DyaK2c%2FpslShsA%3D%3D (Pubitemid 23350479)
36. Sadoc G, Le Masson G, Foutry B, Le Franc Y, Piwkowska Z, Destexhe A, Bal T (2009) Recreating in vivo-like activity and investigating the signal transfer capabilities of neurons: dynamic-clamp applications using real-time NEURON. In: Destexhe A, Bal T (eds) Dynamic-clamp: from principles to applications. Springer, New York
37. AA Sharp MB O'Neil LF Abbott E Marder 1993 Dynamic-clamp: computer-generated conductances in real neurons J Neurophysiol 69 992 995 8463821 1:STN:280:DyaK3s3hslGitQ%3D%3D (Pubitemid 23102967)
38. Traub RD, Miles R (1991) Neuronal networks of the hippocampus. Cambridge University Press, Cambridge
39. W Van Geit E De Schutter P Achard 2008 Automated neuron model optimization techniques: a review Biol Cybern 99 241 251 19011918 10.1007/s00422-008-0257-6 1:STN:280:DC%2BD1cjksVamtQ%3D%3D
40. MC Vanier JM Bower 1999 A comparative survey of automated parameter-search methods for compartmental neural models J Computational Neurosci 7 149 171 10.1023/A:1008972005316 1:STN:280:DyaK1Mvktlaktw%3D%3D (Pubitemid 29433535)
41. CM Weaver SL Wearne 2006 The role of action potential shape and parameter constraints in optimization of compartment models Neurocomputing 69 1053 1057 10.1016/j.neucom.2005.12.044
42. J Wolfart D Debay G Le Masson A Destexhe T Bal 2005 Synaptic background activity controls spike transfer from thalamus to cortex Nature Neurosci 8 1760 1767 16261132 10.1038/nn1591 1:CAS:528:DC%2BD2MXht1Gis77E (Pubitemid 41683205)
43. Yamada WM, Koch C, Adams PR (1989) Multiple channels and calcium dynamics. In: Koch C, Segev I (eds) Methods in neuronal modeling. MIT press, Cambridge