Experiment-Modelling Cycling with Populations of Multi-compartment Models: Application to Hippocampal Interneurons

  • Vladislav Sekulić
  • Frances K. SkinnerEmail author
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI)


Understanding how neurons operate involves investigating how their complements of ion channels interact dynamically along the extent of their somatodendritic trees to produce spiking output appropriate to the cell type in question. This can be approached using experiments where individual ion channel activity is manipulated. However, a large body of experimental and theoretical work has demonstrated that a single neuron may dynamically alter its intrinsic ion channel expression profile in order to maintain output that is required for it to perform its functional role within the network that it is embedded. To appreciate this, a clear sense of the cellular functional role would be required, and this is not usually known. More typically, cellular output for an identified cell type can be characterized and captured in models with different complements of intrinsic properties. In this chapter we propose a cycling approach using experimental data as constraints for building populations of multi-compartment models with a range of ion channel expression patterns that underlie cell-type appropriate model output. These populations or databases can be analyzed to develop predictions regarding the intrinsic property balances for the cell type in question and for the proposed function. Predicted balances and functions can be examined experimentally.


Inhibitory cell Ensemble modelling Ion channel co-regulation O-LM interneuron Theta rhythm h-channels Dendrites 




A subfield in the mammalian hippocampus, referring to “Cornu Ammonis 1,” or the first region in “Amun’s horns,” a name for the hippocampus coined by de Garengeot in the mid-eighteenth century.


Oriens/lacunosum-moleculare; the abbreviation of a type of interneuron in hippocampal CA1/CA3 with soma in stratum oriens and axons projecting to stratum lacunosum/moleculare.


Clutter-based dimension reordering is a technique for the visualization of high-dimensional parameter spaces in 2D (Taylor et al. 2006; Peng et al. 2004).

PANDORA’s toolbox

An open-source MATLAB toolbox that provides an object-oriented framework for assembling and manipulating databases of electrophysiological data, whether from computational models or experiment.


  1. Angelo K, London M, Christensen SR, Häusser M (2007) Local and global effects of Ih distribution in dendrites of mammalian neurons. J Neurosci 27:8643–8653CrossRefGoogle Scholar
  2. Bartos M, Alle H, Vida I (2011) Role of microcircuit structure and input integration in hippocampal interneuron recruitment and plasticity. Neuropharmacology 60:730–739CrossRefGoogle Scholar
  3. Berger T, Larkum ME, Lüscher HR (2001) High Ih channel density in the distal apical dendrite of layer V pyramidal cells increases bidirectional attenuation of EPSPs. J Neurophysiol 85:855–868CrossRefGoogle Scholar
  4. Dayan P, Abbott LF (eds) (2001) Theoretical neuroscience. MIT Press, Cambridge, MAGoogle Scholar
  5. Destexhe A, Rudolph M, Paré D (2003) The high-conductance state of neocortical neurons in vivo. Nat Rev Neurosci 4:739–751CrossRefGoogle Scholar
  6. Druckmann S, Banitt Y, Gidon A, Schurman F, Markram H et al (2007) A novel multiple objective optimization framework for constraining conductance-based neuron models by experimental data. Front Neurosci 1:7–18CrossRefGoogle Scholar
  7. Druckmann S, Berger TK, Schürmann F, Hill S, Markram H, Segev I (2011) Effective stimuli for constructing reliable neuron models. PLoS Comput Biol 7(8):e1002133CrossRefGoogle Scholar
  8. Enyedi P, Czirják G (2010) Molecular background of leak K+ currents: two-pore domain potassium channels. Physiol Rev 90:559–605CrossRefGoogle Scholar
  9. Foster WR, Ungar LH, Schwaber JS (1993) Significance of conductances in Hodgkin-Huxley models. J Neurophysiol 70(6):2502–2518CrossRefGoogle Scholar
  10. Freund TF, Buzsáki G (1996) Interneurons of the hippocampus. Hippocampus 6:347–470CrossRefGoogle Scholar
  11. Golowasch J, Buchholtz F, Epstein IR, Marder E (1992) Contribution of individual ionic currents to activity of a model stomatogastric ganglion neuron. J Neuro-Oncol 67(2):341–349Google Scholar
  12. Golowasch J, Goldman MS, Abbott LF, Marder E (2002) Failure of averaging in the construction of a conductance-based neuron model. J Neurophysiol 87:1129–1131CrossRefGoogle Scholar
  13. Günay C, Edgerton JR, Jaeger D (2008) Channel density distributions explain spiking variability in the globus pallidus: a combined physiology and computer simulation database approach. J Neurosci 28:7476–7491CrossRefGoogle Scholar
  14. Günay C, Edgerton JR, Li S, Sangrey T, Prinz AA et al (2009) Database analysis of simulated and recorded electrophysiological datasets with PANDORA’s Toolbox. Neuroinformatics 7:93–111CrossRefGoogle Scholar
  15. Hay E, Hill S, Schürmann F, Markram H, Segev I (2011) Models of neocortical layer 5b pyramidal cells capturing a wide range of dendritic and perisomatic active properties. PLoS Comput Biol 7(7):e1002107CrossRefGoogle Scholar
  16. Hines ML, Carnevale NT (2001) NEURON: a tool for neuroscientists. Neuroscientist 7:123–135CrossRefGoogle Scholar
  17. Hodgkin AL, Huxley AF (1952) A quantitative description of membrane current and its application to conduction and excitation in nerve. J Physiol 117:500–544CrossRefGoogle Scholar
  18. Holmes WR (2010) Passive cable modeling. In: De Schutter E (ed) Computational modeling methods for neuroscientists. MIT Press, Cambridge, MAGoogle Scholar
  19. Ingber L (1993) Simulated annealing: practice versus theory. Math Comput Model 18(11):29–57CrossRefGoogle Scholar
  20. Johnston D, Narayanan R (2008) Active dendrites: colorful wings of the mysterious butterflies. Trends Neurosci 32(6):309–316CrossRefGoogle Scholar
  21. Keren N, Peled N, Korngreen A (2005) Constraining compartmental models using multiple voltage recordings and genetic algorithms. J Neurophysiol 94:3730–3742CrossRefGoogle Scholar
  22. Koch C, Segev I (eds) (1998) Methods in neuronal modeling. MIT Press, Cambridge, MAGoogle Scholar
  23. Kole MHP, Hallermann S, Stuart GJ (2006) Single I h channels in pyramidal neuron dendrites: properties, distribution, and impact on action potential output. J Neurosci 26:1677–1687CrossRefGoogle Scholar
  24. Kvitsiani D, Ranade S, Hangya B, Taniguchi H, Huang JZ, Kepecs A (2013) Distinct behavioural and network correlates of two interneuron types in prefrontal cortex. Nature 498:363–366CrossRefGoogle Scholar
  25. Lai HC, Jan LY (2006) The distribution and targeting of neuronal voltage-gated ion channels. Nat Rev Neurosci 7:548–562CrossRefGoogle Scholar
  26. Lawrence JJ, Saraga F, Churchill JF, Statland JM, Travis KE et al (2006a) Somatodendritic Kv7/KCNQ/M channels control interspike interval in hippocampal interneurons. J Neurosci 26:12325–12338CrossRefGoogle Scholar
  27. Lawrence JJ, Statland JM, Grinspan ZM, McBain CJ (2006b) Cell type-specific dependence of muscarinic signalling in mouse hippocampal stratum oriens interneurons. J Physiol 570:595–610CrossRefGoogle Scholar
  28. Leão RNR, Mikulović SS, Leão KEK, Munguba HH, Gezelius HH et al (2012) OLM interneurons differentially modulate CA3 and entorhinal inputs to hippocampal CA1 neurons. Nat Neurosci 15:1524–1530CrossRefGoogle Scholar
  29. Lien CC, Martina M, Schultz JH, Ehmke H, Jonas P (2002) Gating, modulation and subunit composition of voltage-gated K+ channels in dendritic inhibitory interneurones of rat hippocampus. J Physiol 538:405–419CrossRefGoogle Scholar
  30. Loken C, Gruner D, Groer L, Peltier R, Bunn N et al (2010) SciNet: lessons learned from building a power-efficient top-20 system and data centre. J Phys Conf Ser 256:012026CrossRefGoogle Scholar
  31. London M, Häusser M (2005) Dendritic computation. Annu Rev Neurosci 28:503–532CrossRefGoogle Scholar
  32. Lovett-Barron M, Kaifosh P, Kheirbek MA, Danielson N, Zaremba JD et al (2014) Dendritic inhibition in the hippocampus supports fear learning. Science 343:857–863CrossRefGoogle Scholar
  33. Maccaferri G, McBain CJ (1996) The hyperpolarization-activated current (Ih) and its contribution to pacemaker activity in rat CA1 hippocampal stratum oriens-alveus interneurones. J Physiol 497:119–130CrossRefGoogle Scholar
  34. MacLean JN, Zhang Y, Johnson BR, Harris-Warrick RM (2003) Activity-independent homeostasis in rhythmically active neurons. Neuron 37:109–120CrossRefGoogle Scholar
  35. Magee JC (1998) Dendritic hyperpolarization-activated currents modify the integrative properties of hippocampal CA1 pyramidal neurons. J Neurosci 18:7613–7624CrossRefGoogle Scholar
  36. Mainen Z, Sejnowski TJ (1996) Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382:363–366CrossRefGoogle Scholar
  37. Marder E, Bucher D (2007) Understanding circuit dynamics using the stomatogastric nervous system of lobsters and crabs. Annu Rev Physiol 69:291–316CrossRefGoogle Scholar
  38. Marder E, Goaillard JM (2006) Variability, compensation and homeostasis in neuron and network function. Nat Rev Neurosci 7:536–574CrossRefGoogle Scholar
  39. Marder E, Taylor AL (2011) Multiple models to capture the variability in biological neurons and networks. Nat Neurosci 14(2):133–138CrossRefGoogle Scholar
  40. Martina M, Vida I, Jonas P (2000) Distal initiation and active propagation of action potentials in interneuron dendrites. Science 287:295–300CrossRefGoogle Scholar
  41. Matt L, Michalakis S, Hofmann F, Hammelmann V, Ludwig A et al (2011) HCN2 channels in local inhibitory interneurons constrain LTP in the hippocampal direct perforant path. Cell Mol Life Sci 68:125–137CrossRefGoogle Scholar
  42. Migliore M, Migliore R (2012) Know your current Ih: interaction with a shunting current explains the puzzling effects of its pharmacological or pathological modulations. PLoS One 7(5):e36867CrossRefGoogle Scholar
  43. Narayanan R, Johnston D (2012) Functional maps within a single neuron. J Neurophysiol 108:2343–2351CrossRefGoogle Scholar
  44. Niebur E (2008) Neuronal cable theory. Scholarpedia 3(5):2674CrossRefGoogle Scholar
  45. O’Leary T, Williams AH, Caplan JS, Marder E (2013) Correlations in ion channel expression emerge from homeostatic tuning rules. Proc Natl Acad Sci U S A 110(28):E2645–E2654CrossRefGoogle Scholar
  46. Otchy TM, Wolff SBE, Rhee JY, Pehlevan C, Kawai R et al (2015) Acute off-target effects of neural circuit manipulations. Nature 528:348–363CrossRefGoogle Scholar
  47. Peng W, Ward MO, Rundensteiner EA (2004) Clutter reduction in multi-dimensional data visualization using dimensional reordering. In: Keahey A (ed) Proceedings of the IEEE symposium on information visualization 2004. Austin, TX, pp 89–96Google Scholar
  48. Perez Y, Morin F, Lacaille JC (2001) A hebbian form of long-term potentiation dependent on mGluR1a in hippocampal inhibitory interneurons. Proc Natl Acad Sci U S A 98:9401–9406CrossRefGoogle Scholar
  49. Pi HJ, Hangya B, Kvitsiani D, Sanders JI, Huang ZJ, Kepecs A (2013) Cortical interneurons that specialize in disinhibitory control. Nature 503:521–524CrossRefGoogle Scholar
  50. Prinz AA, Billimora CP, Marder E (2003) Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J Neurophysiol 90:3998–4015CrossRefGoogle Scholar
  51. Prinz AA, Bucher D, Marder E (2004) Similar network activity from disparate circuit parameters. Nat Neurosci 7(12):1345–1352CrossRefGoogle Scholar
  52. Rall W (2009) Rall model. Scholarpedia 4(4):1369CrossRefGoogle Scholar
  53. Rall W, Burke RE, Holmes WR, Jack JJB, Redman SJ, Segev I (1992) Matching dendritic neuron models to experimental data. Phys Rev 72(4):S159–S186Google Scholar
  54. Rotstein HG, Pervouchine DD, Acker CD, Gillies MJ, White JA et al (2005) Slow and fast inhibition and an H-current interact to create a theta rhythm in a model of CA1 interneuron network. J Neurophysiol 94:1509–1518CrossRefGoogle Scholar
  55. Royer S, Zemelman BV, Losonczy A, Kim J, Chance F, Magee JC, Buzsáki G (2012) Control of timing, rate and bursts of hippocampal place cells by dendritic and somatic inhibition. Nat Neurosci 15:769–775CrossRefGoogle Scholar
  56. Saraga F, Wu CP, Zhang L, Skinner FK (2003) Active dendrites and spike propagation in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons. J Physiol 552(3):673–689CrossRefGoogle Scholar
  57. Schulz DJ, Goaillard JM, Marder E (2006) Variable channel expression in identified single and electrically coupled neurons in different animals. Nat Neurosci 9:356–362CrossRefGoogle Scholar
  58. Segev I, London M (2000) Untangling dendrites with quantitative models. Science 290:744–750CrossRefGoogle Scholar
  59. Sekulić V, Lawrence JJ, Skinner FK (2014) Using multi-compartment ensemble modeling as an investigative tool of spatially distributed biophysical balances: application to hippocampal oriens-lacunosum/moleculare (O-LM) cells. PLoS One 9(10):e106567CrossRefGoogle Scholar
  60. Sekulić V, Chen TC, Lawrence JJ, Skinner FK (2015) Dendritic distributions of I h channels in experimentally-derived multi-compartment models of oriens-lacunosum/moleculare (O-LM) hippocampal interneurons. Front Syn Neurosci 7(2):1–15Google Scholar
  61. Sik A, Penttonen M, Ylinen A, Buzsáki G (1995) Hippocampal CA1 interneurons: an in vivo intracellular labeling study. J Neurosci 15:6651–6665CrossRefGoogle Scholar
  62. Skinner FK (2006) Conductance-based models. Scholarpedia 1(11):1408CrossRefGoogle Scholar
  63. Skinner FK, Saraga F (2010) Single neuron models: interneurons. In: Hippocampal microcircuits: a computational Modeler’s resource book. Springer, New York, pp 399–422CrossRefGoogle Scholar
  64. Smolinski TG, Prinz AA (2009) Computational intelligence in modeling of biological neurons: a case study of an invertebrate pacemaker neuron. IEEE Proc Intl Jt Conf Neural Netw:2964–2970Google Scholar
  65. Swensen AM, Bean BP (2005) Robustness of burst firing in dissociated purkinje neurons with acute or long-term reductions in sodium conductance. J Neurosci 25:3509–3520CrossRefGoogle Scholar
  66. Talley EM, Solorzano G, Lei Q, Kim D, Bayliss DA (2001) CNS distribution of members of the two-pore-domain (KCNK) potassium channel family. J Neurosci 21:7491–7505CrossRefGoogle Scholar
  67. Taylor AL, Hickey TJ, Prinz AA, Marder E (2006) Structure and visualization of high-dimensional conductance spaces. J Neurophysiol 96:891–905CrossRefGoogle Scholar
  68. Torborg CL, Berg AP, Jeffries BW, Bayliss DA, McBain CJ (2006) TASK-like conductances are present within hippocampal CA1 stratum oriens interneuron subpopulations. J Neurosci 26:7362–7367CrossRefGoogle Scholar
  69. Traub RD, Jefferys JGR, Miles R, Whittington MA, Tóth K (1994) A branching dendritic model of a rodent CA3 pyramidal neuron. J Physiol 481:79–95CrossRefGoogle Scholar
  70. Van Geit W, De Schutter E, Achard P (2008) Automated neuron model optimization techniques: a review. Biol Cybern 99:241–251CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Krembil Research Institute, University Health NetworkTorontoCanada
  2. 2.Department of PhysiologyUniversity of TorontoTorontoCanada
  3. 3.Departments of Medicine (Neurology) and PhysiologyUniversity of TorontoTorontoCanada

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