Abstract
We review here the development of Hodgkin–Huxley (HH) type models of cerebral cortex and thalamic neurons for network simulations. The intrinsic electrophysiological properties of cortical neurons were analyzed from several preparations, and we selected the four most prominent electrophysiological classes of neurons. These four classes are “fast spiking” “regular spiking” “intrinsically bursting” and “low-threshold spike” cells. For each class, we fit “minimal” HH type models to experimental data. The models contain the minimal set of voltage-dependent currents to account for the data. To obtain models as generic as possible, we used data from different preparations in vivo and in vitro, such as rat somatosensory cortex and thalamus, guinea-pig visual and frontal cortex, ferret visual cortex, cat visual cortex and cat association cortex. For two cell classes, we used automatic fitting procedures applied to several cells, which revealed substantial cell-to-cell variability within each class. The selection of such cellular models constitutes a necessary step towards building network simulations of the thalamocortical system with realistic cellular dynamical properties.
Similar content being viewed by others
References
Achard P, De Schutter E (2006) Complex parameter landscape for a complex neuron model. PLoS Comput Biol 2: e94
Baldi P, Vanier MC, Bower JM (1998) On the use of Bayesian methods for evaluating compartmental neural models. J Comput Neurosci 5: 285–314
Bhalla US, Bower JM (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
Brette R, Gerstner W (2005) Adaptive exponential integrate-and-fire model as an effective description of neuronal activity. J Neurophysiol 94: 3637–3642
Cauller LJ, Connors BW (1992) Functions of very distal dendrites: experimental and computational studies of Layer I synapses on neocortical pyramidal cells. In: McKenna T, Davis J, Zornetzer SF (eds) Single neuron computation. Academic Press, Boston
Connors BW, Gutnick MJ (1990) Intrinsic firing patterns of diverse neocortical neurons. Trends Neurosci 13: 99–104
Contreras D, Steriade M (1995) Cellular basis of EEG slow rhythms: a study of dynamic corticothalamic relationships. J Neurosci 15: 604–622
de la Peña E, Geijo-Barrientos E (1996) Laminar organization, morphology and physiological properties of pyramidal neurons that have the low-threshold calcium current in the guinea-pig frontal cortex. J Neurosci 16: 5301–5311
Destexhe A (2001) Simplified models of neocortical pyramidal cells preserving somatodendritic voltage attenuation. Neurocomputing 38: 167–173
Destexhe A, Bal T, McCormick DA, Sejnowski TJ (1996) Ionic mechanisms underlying synchronized oscillations and propagating waves in a model of ferret thalamic slices. J Neurophysiol 76: 2049–2070
Destexhe A, Contreras D, Steriade M, Sejnowski TJ, Huguenard JR (1996) In vivo, in vitro and computational analysis of dendritic calcium currents in thalamic reticular neurons. J Neurosci 16: 169–185
Destexhe A, Neubig M, Ulrich D, Huguenard JR (1998) Dendritic low-threshold calcium currents in thalamic relay cells. J Neurosci 18: 3574–3588
Destexhe A, Contreras D, Steriade M (2001) LTS cells in cerebral cortex and their role in generating spike-and-wave oscillations. Neurocomputing 38: 555–563
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. Front Neurosci 1: 7–18
Eichler-West R, Wilcox G (1997) Robust parameter selection for compartmental models of neurons using evolutionary algorithms. In: Bower JM (eds) Computational neuroscience: trends in research 1997. Plenum Press, New York, pp 75–80
Foster WR, Ungar LH, Schwaber JS (1993) Significance of conductances in Hodgkin–Huxley models. J Neurophysiol 70: 2502–2518
Golowasch J, Goldman MS, Abbott LF, Marder E (2002) Failure of averaging in the construction of a conductance-based neuron model. J Neurophysiol 87: 1129–1131
Gray CM, McCormick DA (1996) Chattering cells: superficial pyramidal neurons contributing to the generation of synchronous oscillations in the visual cortex. Science 274: 109–113
Gupta A, Wang Y, Markram H (2000) Organizing principles for a diversity of GABAergic interneurons and synapses in the neocortex. Science 287: 273–278
Haufler D, Morinc F, Lacaille JC, Skinner FK (2007) Parameter estimation in single-compartment neuron models using a synchronization-based method. Neurocomputing 70: 1605–1610
Hines ML, Carnevale NT (1997) The neuron simulation environment. Neural Comput 9: 1179–1209
Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117: 500–544
Holmes W, Ambros-Ingerson J, Grover L (2006) Fitting experimental data to models that use morphological data from public databases. J Computat Neurosci 20: 349–365
Huguenard JR, McCormick DA (1992) Simulation of the currents involved in rhythmic oscillations in thalamic relay neurons. J Neurophysiol 68: 1373–1383
Huguenard JR, Prince DA (1992) A novel T-type current underlies prolonged Ca2+-dependent bursts firing in GABAergic neurons of rat thalamic reticular nucleus. J Neurosci 12: 3804–3817
Izhikevich EM (2004) Which model to use for cortical spiking neurons?. IEEE Trans Neural Netw 15: 1063–1070
Jolivet R, Lewis TJ, Gerstner W (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
LeMasson G, Maex R (2001) Introduction to equation solving and parameter fitting. In: De Schutter E (eds) Computational neuroscience: realistic modeling for experimentalists. CRC Press, Boca Raton, pp 1–22
Llinás RR (1988) The intrinsic electrophysiological properties of mammalian neurons: a new insight into CNS function. Science 242: 1654–1664
Major G, Larkmann AU, Jonas P, Sakmann B, Jack JJB (1994) Detailed passive cable models of whole-cell recorded CA3 pyramidal neurons in rat hippocampal slices. J Neurosci 14: 4613–4638
Marder E, Tobin AE, Grashow R (2007) How tightly tuned are network parameters? Insight from computational and experimental studies in small rhythmic motor networks. Prog Brain Res 165: 193–200
McCormick DA, Connors BW, Lighthall JW, Prince DA (1985) Comparative electrophysiology of pyramidal and sparsely spiny stellate neurons of the neocortex. J Neurophysiol 54: 782–806
Monier C, Chavane F, Baudot P, Graham LJ, Frégnac Y (2003) Orientation and direction selectivity of synaptic inputs in visual cortical neurons: a diversity of combinations produces spike tuning. Neuron 37: 663–680
Press WH, Flannery BP, Teukolsky SA (1992) Numerical recipes in C: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge
Rall W, Burke RE, Holmes WR, Jack JJ, Redman SJ, Segev I (1992) Matching dendritic neuron models to experimental data. Physiol Rev 72: S159–S186
Rapp M, Segev I, Yarom Y (1994) Physiology, morphology and detailed passive models of guinea-pig cerebellar Purkinje cells. J Physiol 474: 101–118
Rauch A, La Camera G, Lüscher H-R, Senn W, Fusi S (2003) Neocortical pyramidal cells respond as integrate-and-fire neurons to in vivo like input currents. J Neurophysiol 90: 1598–1612
Renaud S, Tomas J, Bornat Y, Daouzli A, Saïghi S (2007) Neuromimetic ICs with analog cores: an alternative for simulating spiking neural networks. In: International symposium on circuits and systems (ISCAS07), New-Orleans, USA, 27–30 May (ISBN 1-4244-0921-1), pp 3355–358
Reuveni I, Friedman A, Amitai Y, Gutnick MJ (1993) Stepwise repolarization from Ca2+ plateaus in neocortical pyramidal cells: evidence for nonhomogeneous distribution of HVA Ca2+ channels in dendrites. J Neurosci 13: 4609–4621
Rinzel J (1987) A formal classification of bursting mechanisms in excitable systems. In: Teramoto E, Yamaguti M (eds) Mathematical topics in population biology, morphogenesis and neurosciences. Springer, Berlin, pp 267–281
Rinzel J, Ermentrout GB (1989) Analysis of neural excitability and oscillations. In: Koch C, Segev I (eds) Methods in neuronal modeling. MIT press, Cambridge, pp 135–169
Rose RM, Hindmarsh JL (1989) The assembly of ionic currents in a thalamic neuron. I. The three-dimensional model. Proc R Soc Lond B Biol Sci 237: 267–288
Sayer RJ, Schwindt PC, Crill WE (1990) High- and low-threshold calcium currents in neurons acutely isolated from rat sensorimotor cortex. Neurosci Lett 120: 175–178
Shu Y, Hasenstaub A, Badoual M, Bal T, McCormick DA (2003) Barrages of synaptic activity control the gain and sensitivity of cortical neurons. J Neurosci 23: 10388–10401
Smith GD, Cox CL, Sherman M, Rinzel J (2000) Fourier analysis of sinusoidally driven thalamocortical relay neurons and a minimal integrate-and-fire-or-burst model. J Neurophysiol 83: 588–610
Steriade M, Timofeev I, Durmüller N, Grenier F (1998) Dynamic properties of corticothalamic neurons and local cortical interneurons generating fast rhythmic (30–40 Hz) spike bursts. J Neurophysiol 79: 483–490
Stratford K, Mason A, Larkman A, Major G, Jack J (1989) The modeling of pyramidal neurones in the visual cortex. In: Durbin A, Miall C, Mitchison G (eds) The computing neuron. Addison-Wesley, Workingham, pp 296–321
Stuart G, Spruston N (1998) Determinants of voltage attenuation in neocortical pyramidal neuron dendrites. J Neurosci 18: 3501–3510
Tawfik B, Durand DM (1994) Nonlinear parameter-estimation by linear associationapplication to a 5-parameter passive neuron model. IEEE Trans Biomed Eng 41: 461–469
Taylor AL, Hickey TJ, Prinz AA, Marder E (2006) Structure and visualization of high-dimensional conductance spaces. J Neurophysiol 96: 891–905
Tien JH, Guckenheimer J (2008) Parameter estimation for bursting neural models. J Computat Neurosci 24: 358–373
Toledo-Rodriguez M, Blumenfeld B, Wu C, Luo J, Attali B, Goodman P, Markram H (2004) Correlation maps allow neuronal electrical properties to be predicted from single-cell gene expression profiles in rat neocortex. Cereb Cortex 14: 1310–1327
Traub RD, Miles R (1991) Neuronal networks of the Hippocampus. Cambridge University Press, Cambridge
Vanier MC, Bower JM (1999) A comparative survey of automated parameter-search methods for compartmental neural models. J Comput Neurosci 7: 149–171
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, pp 97–134
Zou Q, Bornat Y, Saïghi S, Tomas J, Renaud S, Destexhe A (2006) Analog-digital simulations of full conductance-based networks of spiking neurons with spike timing dependent plasticity. Network 17: 211–233
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Pospischil, M., Toledo-Rodriguez, M., Monier, C. et al. Minimal Hodgkin–Huxley type models for different classes of cortical and thalamic neurons. Biol Cybern 99, 427–441 (2008). https://doi.org/10.1007/s00422-008-0263-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00422-008-0263-8