Skip to main content
SpringerLink
Log in

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

  • Chapter
  • First Online:
Hippocampal Microcircuits

Part of the book series: Springer Series in Computational Neuroscience ((NEUROSCI))

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Angelo K, London M, Christensen SR, Häusser M (2007) Local and global effects of Ih distribution in dendrites of mammalian neurons. J Neurosci 27:8643–8653

    Article  CAS  Google Scholar 

  • Bartos M, Alle H, Vida I (2011) Role of microcircuit structure and input integration in hippocampal interneuron recruitment and plasticity. Neuropharmacology 60:730–739

    Article  CAS  Google Scholar 

  • 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 85:855–868

    Article  CAS  Google Scholar 

  • Dayan P, Abbott LF (eds) (2001) Theoretical neuroscience. MIT Press, Cambridge, MA

    Google Scholar 

  • Destexhe A, Rudolph M, Paré D (2003) The high-conductance state of neocortical neurons in vivo. Nat Rev Neurosci 4:739–751

    Article  CAS  Google Scholar 

  • 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 1:7–18

    Article  Google Scholar 

  • 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):e1002133

    Article  CAS  Google Scholar 

  • Enyedi P, Czirják G (2010) Molecular background of leak K+ currents: two-pore domain potassium channels. Physiol Rev 90:559–605

    Article  CAS  Google Scholar 

  • Foster WR, Ungar LH, Schwaber JS (1993) Significance of conductances in Hodgkin-Huxley models. J Neurophysiol 70(6):2502–2518

    Article  CAS  Google Scholar 

  • Freund TF, Buzsáki G (1996) Interneurons of the hippocampus. Hippocampus 6:347–470

    Article  CAS  Google Scholar 

  • 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):341–349

    CAS  Google Scholar 

  • 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

    Article  Google Scholar 

  • 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 28:7476–7491

    Article  Google Scholar 

  • 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 7:93–111

    Article  Google Scholar 

  • 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):e1002107

    Article  CAS  Google Scholar 

  • Hines ML, Carnevale NT (2001) NEURON: a tool for neuroscientists. Neuroscientist 7:123–135

    Article  CAS  Google Scholar 

  • 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

    Article  CAS  Google Scholar 

  • Holmes WR (2010) Passive cable modeling. In: De Schutter E (ed) Computational modeling methods for neuroscientists. MIT Press, Cambridge, MA

    Google Scholar 

  • Ingber L (1993) Simulated annealing: practice versus theory. Math Comput Model 18(11):29–57

    Article  Google Scholar 

  • Johnston D, Narayanan R (2008) Active dendrites: colorful wings of the mysterious butterflies. Trends Neurosci 32(6):309–316

    Article  Google Scholar 

  • Keren N, Peled N, Korngreen A (2005) Constraining compartmental models using multiple voltage recordings and genetic algorithms. J Neurophysiol 94:3730–3742

    Article  Google Scholar 

  • Koch C, Segev I (eds) (1998) Methods in neuronal modeling. MIT Press, Cambridge, MA

    Google Scholar 

  • 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 26:1677–1687

    Article  CAS  Google Scholar 

  • 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 498:363–366

    Article  CAS  Google Scholar 

  • Lai HC, Jan LY (2006) The distribution and targeting of neuronal voltage-gated ion channels. Nat Rev Neurosci 7:548–562

    Article  CAS  Google Scholar 

  • 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 26:12325–12338

    Article  CAS  Google Scholar 

  • Lawrence JJ, Statland JM, Grinspan ZM, McBain CJ (2006b) Cell type-specific dependence of muscarinic signalling in mouse hippocampal stratum oriens interneurons. J Physiol 570:595–610

    Article  CAS  Google Scholar 

  • 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 15:1524–1530

    Article  Google Scholar 

  • 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 538:405–419

    Article  CAS  Google Scholar 

  • 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 256:012026

    Article  Google Scholar 

  • London M, Häusser M (2005) Dendritic computation. Annu Rev Neurosci 28:503–532

    Article  CAS  Google Scholar 

  • Lovett-Barron M, Kaifosh P, Kheirbek MA, Danielson N, Zaremba JD et al (2014) Dendritic inhibition in the hippocampus supports fear learning. Science 343:857–863

    Article  CAS  Google Scholar 

  • 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 497:119–130

    Article  CAS  Google Scholar 

  • MacLean JN, Zhang Y, Johnson BR, Harris-Warrick RM (2003) Activity-independent homeostasis in rhythmically active neurons. Neuron 37:109–120

    Article  CAS  Google Scholar 

  • Magee JC (1998) Dendritic hyperpolarization-activated currents modify the integrative properties of hippocampal CA1 pyramidal neurons. J Neurosci 18:7613–7624

    Article  CAS  Google Scholar 

  • Mainen Z, Sejnowski TJ (1996) Influence of dendritic structure on firing pattern in model neocortical neurons. Nature 382:363–366

    Article  CAS  Google Scholar 

  • Marder E, Bucher D (2007) Understanding circuit dynamics using the stomatogastric nervous system of lobsters and crabs. Annu Rev Physiol 69:291–316

    Article  CAS  Google Scholar 

  • Marder E, Goaillard JM (2006) Variability, compensation and homeostasis in neuron and network function. Nat Rev Neurosci 7:536–574

    Article  Google Scholar 

  • Marder E, Taylor AL (2011) Multiple models to capture the variability in biological neurons and networks. Nat Neurosci 14(2):133–138

    Article  CAS  Google Scholar 

  • Martina M, Vida I, Jonas P (2000) Distal initiation and active propagation of action potentials in interneuron dendrites. Science 287:295–300

    Article  CAS  Google Scholar 

  • 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 68:125–137

    Article  CAS  Google Scholar 

  • 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):e36867

    Article  CAS  Google Scholar 

  • Narayanan R, Johnston D (2012) Functional maps within a single neuron. J Neurophysiol 108:2343–2351

    Article  Google Scholar 

  • Niebur E (2008) Neuronal cable theory. Scholarpedia 3(5):2674

    Article  Google Scholar 

  • 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–E2654

    Article  Google Scholar 

  • Otchy TM, Wolff SBE, Rhee JY, Pehlevan C, Kawai R et al (2015) Acute off-target effects of neural circuit manipulations. Nature 528:348–363

    Article  Google Scholar 

  • 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 89–96

    Google Scholar 

  • 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 98:9401–9406

    Article  CAS  Google Scholar 

  • Pi HJ, Hangya B, Kvitsiani D, Sanders JI, Huang ZJ, Kepecs A (2013) Cortical interneurons that specialize in disinhibitory control. Nature 503:521–524

    Article  CAS  Google Scholar 

  • Prinz AA, Billimora CP, Marder E (2003) Alternative to hand-tuning conductance-based models: construction and analysis of databases of model neurons. J Neurophysiol 90:3998–4015

    Article  Google Scholar 

  • Prinz AA, Bucher D, Marder E (2004) Similar network activity from disparate circuit parameters. Nat Neurosci 7(12):1345–1352

    Article  CAS  Google Scholar 

  • Rall W (2009) Rall model. Scholarpedia 4(4):1369

    Article  Google Scholar 

  • 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–S186

    CAS  Google Scholar 

  • 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 94:1509–1518

    Article  Google Scholar 

  • 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 15:769–775

    Article  CAS  Google Scholar 

  • 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):673–689

    Article  CAS  Google Scholar 

  • Schulz DJ, Goaillard JM, Marder E (2006) Variable channel expression in identified single and electrically coupled neurons in different animals. Nat Neurosci 9:356–362

    Article  CAS  Google Scholar 

  • Segev I, London M (2000) Untangling dendrites with quantitative models. Science 290:744–750

    Article  CAS  Google Scholar 

  • 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):e106567

    Article  Google Scholar 

  • 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):1–15

    Google Scholar 

  • Sik A, Penttonen M, Ylinen A, Buzsáki G (1995) Hippocampal CA1 interneurons: an in vivo intracellular labeling study. J Neurosci 15:6651–6665

    Article  CAS  Google Scholar 

  • Skinner FK (2006) Conductance-based models. Scholarpedia 1(11):1408

    Article  Google Scholar 

  • Skinner FK, Saraga F (2010) Single neuron models: interneurons. In: Hippocampal microcircuits: a computational Modeler’s resource book. Springer, New York, pp 399–422

    Chapter  Google Scholar 

  • 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:2964–2970

    Google Scholar 

  • Swensen AM, Bean BP (2005) Robustness of burst firing in dissociated purkinje neurons with acute or long-term reductions in sodium conductance. J Neurosci 25:3509–3520

    Article  CAS  Google Scholar 

  • 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 21:7491–7505

    Article  CAS  Google Scholar 

  • Taylor AL, Hickey TJ, Prinz AA, Marder E (2006) Structure and visualization of high-dimensional conductance spaces. J Neurophysiol 96:891–905

    Article  Google Scholar 

  • 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 26:7362–7367

    Article  CAS  Google Scholar 

  • Traub RD, Jefferys JGR, Miles R, Whittington MA, Tóth K (1994) A branching dendritic model of a rodent CA3 pyramidal neuron. J Physiol 481:79–95

    Article  CAS  Google Scholar 

  • Van Geit W, De Schutter E, Achard P (2008) Automated neuron model optimization techniques: a review. Biol Cybern 99:241–251

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Krembil Research Institute, University Health Network, Toronto, ON, Canada

    Vladislav Sekulić & Frances K. Skinner

  2. Department of Physiology, University of Toronto, Toronto, ON, Canada

    Vladislav Sekulić

  3. Departments of Medicine (Neurology) and Physiology, University of Toronto, Toronto, ON, Canada

    Frances K. Skinner

Authors
  1. Vladislav Sekulić
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frances K. Skinner
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Frances K. Skinner .

Editor information

Editors and Affiliations

  1. School of Computer Science, University of Lincoln, Lincoln, UK

    Vassilis Cutsuridis

  2. School of Natural Sciences, Department of Computing Science and Mathematics, University of Stirling, Stirling, UK

    Bruce P. Graham

  3. Centre for Discovery Brain Sciences, University of Edinburgh, Edinburgh, UK

    Stuart Cobb

  4. Institute for Integrative Neuroanatomy, Charité – Universitätsmedizin Berlin, Berlin, Germany

    Imre Vida

Glossary

CA1

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.

O-LM

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.

CBDR

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.

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sekulić, V., Skinner, F.K. (2018). Experiment-Modelling Cycling with Populations of Multi-compartment Models: Application to Hippocampal Interneurons. In: Cutsuridis, V., Graham, B., Cobb, S., Vida, I. (eds) Hippocampal Microcircuits. Springer Series in Computational Neuroscience. Springer, Cham. https://doi.org/10.1007/978-3-319-99103-0_25

Download citation

Publish with us

Policies and ethics

Search

Navigation