Skip to main content
View expanded cover

International Conference on Intelligent Computing

ICIC 2017: Intelligent Computing Theories and Application pp 157–168Cite as

Modeling Neuron-Astrocyte Interactions: Towards Understanding Synaptic Plasticity and Learning in the Brain

Part of the Lecture Notes in Computer Science book series (LNISA,volume 10362)

Abstract

Spiking neural networks represent a third generation of artificial neural networks and are inspired by computational principles of neurons and synapses in the brain. In addition to neuronal mechanisms, astrocytic signaling can influence information transmission, plasticity and learning in the brain. In this study, we developed a new computational model to better understand the dynamics of mechanisms that lead to changes in information processing between a postsynaptic neuron and an astrocyte. We used a classical stimulation protocol of long-term plasticity to test the model functionality. The long-term goal of our work is to develop extended synapse models including neuron-astrocyte interactions to address plasticity and learning in cortical synapses. Our modeling studies will advance the development of novel learning algorithms to be used in the extended synapse models and spiking neural networks. The novel algorithms can provide a basis for artificial intelligence systems that can emulate the functionality of mammalian brain.

Keywords

  • Astrocyte
  • Neuron
  • Calcium
  • Computational model
  • Synaptic plasticity
  • Learning

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-63312-1_14
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   109.00
Price excludes VAT (USA)
  • ISBN: 978-3-319-63312-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   139.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.

References

  1. Turrigiano, G.G., Leslie, K.R., Desai, N.S., Rutherford, L.C., Nelson, S.B.: Activity-dependent scaling of quantal amplitude in neocortical neurons. Nature 391(6670), 892–896 (1998)

    CrossRef  Google Scholar 

  2. Hebb, D.O.: The Organization of Behavior: A Neuropsychological Theory. Wiley, New York (1949)

    Google Scholar 

  3. Lømo, T.: Frequency potentiation of excitatory synaptic activity in dentate area of hippocampal formation. Acta Physiol. Scand. 68(Suppl 277), 128 (1966)

    Google Scholar 

  4. Bliss, T.V., Lømo, T.: Plasticity in a monosynaptic cortical pathway. J. Physiol. 207(2), 61P (1970)

    Google Scholar 

  5. Bliss, T.V.P., Lømo, T.: Long-lasting potentiation of synaptic transmission in the dentate area of the anaesthetized rabbit following stimulation of the perforant path. J. Physiol. 232(2), 331–356 (1973)

    CrossRef  Google Scholar 

  6. Douglas, R.M., Goddard, G.V.: Long-term potentiation of the perforant path-granule cell synapse in the rat hippocampus. Brain Res. 86(2), 205–215 (1975)

    CrossRef  Google Scholar 

  7. Steinbuch, K., Jaenicke, W., Reiner, H.: Learning matrix. C.I.P. Office (1965). http://brevets-patents.ic.gc.ca/opic-cipo/cpd/eng/patent/717227/summary.html

  8. Willshaw, D.J., Buneman, O.P., Longuet-Higgins, H.C.: Non-holographic associative memory. Nature 222, 960–962 (1969)

    CrossRef  Google Scholar 

  9. Kohonen, T.: Correlation matrix memories. IEEE Trans. Comput. C-21(4), 353–359 (1972)

    CrossRef  MATH  Google Scholar 

  10. Hopfield, J.J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. U.S.A. 79(8), 2554–2558 (1982)

    MathSciNet  CrossRef  Google Scholar 

  11. Markram, H., Lübke, J., Frotscher, M., Sakmann, B.: Regulation of synaptic efficacy by coincidence of postsynaptic APs and EPSPs. Science 275(5297), 213–215 (1997)

    CrossRef  Google Scholar 

  12. Bi, G.Q., Poo, M.M.: Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18(24), 10464–10472 (1998)

    Google Scholar 

  13. Bi, G.Q., Poo, M.M.: Synaptic modification by correlated activity: Hebb’s postulate revisited. Annu. Rev. Neurosci. 24(1), 139–166 (2001)

    CrossRef  Google Scholar 

  14. Golding, N.L., Staff, N.P., Spruston, N.: Dendritic spikes as a mechanism for cooperative long-term potentiation. Nature 418(6895), 326–331 (2002)

    CrossRef  Google Scholar 

  15. Häusser, M., Mel, B.: Dendrites: bug or feature? Curr. Opin. Neurobiol. 13(3), 372–383 (2003)

    CrossRef  Google Scholar 

  16. Froemke, R.C., Letzkus, J.J., Kampa, B.M., Hang, G.B., Stuart, G.J.: Dendritic synapse location and neocortical spike-timing-dependent plasticity. Front. Syn. Neurosci. 2, 29 (2010)

    Google Scholar 

  17. Letzkus, J.J., Kampa, B.M., Stuart, G.J.: Learning rules for spike timing-dependent plasticity depend on dendritic synapse location. J. Neurosci. 26(41), 10420–10429 (2006)

    CrossRef  Google Scholar 

  18. Sjöström, P.J., Rancz, E.A., Roth, A., Häusser, M.: Dendritic excitability and synaptic plasticity. Physiol. Rev. 88(2), 769–840 (2008)

    CrossRef  Google Scholar 

  19. Wittenberg, G.M., Wang, S.S.H.: Malleability of spike-timing-dependent plasticity at the CA3–CA1 synapse. J. Neurosci. 26(24), 6610–6617 (2006)

    CrossRef  Google Scholar 

  20. Buchanan, K.A., Mellor, J.R.: The activity requirements for spike timing-dependent plasticity in the hippocampus. Front. Syn. Neurosci. 2, 11 (2010)

    CrossRef  Google Scholar 

  21. Volterra, A., Liaudet, N., Savtchouk, I.: Astrocyte Ca2+ signalling: an unexpected complexity. Nat. Rev. Neurosci. 15(5), 327–335 (2014)

    CrossRef  Google Scholar 

  22. Magistretti, P.J., Allaman, I.: A cellular perspective on brain energy metabolism and functional imaging. Neuron 86(4), 883–901 (2015)

    CrossRef  Google Scholar 

  23. Dossi, E., Vasile, F., Rouach, N.: Human astrocytes in the diseased brain. Brain Res. Bull. (2017, in Press)

    Google Scholar 

  24. De Pittà, M., Brunel, N.: Modulation of synaptic plasticity by glutamatergic gliotransmission: a modeling study. Neural Plast. 2016, 7607924 (2016)

    CrossRef  Google Scholar 

  25. Bushong, E.A., Martone, M.E., Jones, Y.Z., Ellisman, M.H.: Protoplasmic astrocytes in CA1 stratum radiatum occupy separate anatomical domains. J. Neurosci. 22(1), 183–192 (2002)

    Google Scholar 

  26. Pirttimaki, T.M., Hall, S.D., Parri, H.R.: Sustained neuronal activity generated by glial plasticity. J. Neurosci. 31(21), 7637–7647 (2011)

    CrossRef  Google Scholar 

  27. Manninen, T., Havela, R., Linne, M.L.: Computational models of astrocytes and astrocyte-neuron interactions: characterization, reproducibility, and future perspectives. In: De Pittà, M., Berry, H. (eds.) Computational Glioscience. Springer (2017, in Press)

    Google Scholar 

  28. Manninen, T., Havela, R., Linne, M.L.: Reproducibility and comparability of computational models for astrocyte calcium excitability. Front. Neuroinform. 11, 11 (2017)

    CrossRef  Google Scholar 

  29. Tewari, S., Majumdar, K.: A mathematical model for astrocytes mediated LTP at single hippocampal synapses. J. Comput. Neurosci. 33(2), 341–370 (2012)

    MathSciNet  CrossRef  Google Scholar 

  30. Tewari, S.G., Majumdar, K.K.: A mathematical model of the tripartite synapse: astrocyte-induced synaptic plasticity. J. Biol. Phys. 38(3), 465–496 (2012)

    CrossRef  Google Scholar 

  31. Pinsky, P.F., Rinzel, J.: Intrinsic and network rhythmogenesis in a reduced Traub model for CA3 neurons. J. Comput. Neurosci. 1(1), 39–60 (1994)

    CrossRef  Google Scholar 

  32. Sarid, L., Bruno, R., Sakmann, B., Segev, I., Feldmeyer, D.: Modeling a layer 4-to-layer 2/3 module of a single column in rat neocortex: interweaving in vitro and in vivo experimental observations. Proc. Natl. Acad. Sci. U.S.A. 104(41), 16353–16358 (2007)

    CrossRef  Google Scholar 

  33. Zachariou, M., Alexander, S.P.H., Coombes, S., Christodoulou, C.: A biophysical model of endocannabinoid-mediated short term depression in hippocampal inhibition. PLoS ONE 8(3), e58296 (2013)

    CrossRef  Google Scholar 

  34. Politi, A., Gaspers, L.D., Thomas, A.P., Höfer, T.: Models of IP3 and Ca2+ oscillations: frequency encoding and identification of underlying feedbacks. Biophys. J. 90(9), 3120–3133 (2006)

    CrossRef  Google Scholar 

  35. Destexhe, A., Mainen, Z.F., Sejnowski, T.J.: Kinetic models of synaptic transmission. In: Koch, C., Segev, I. (eds.) Methods in Neuronal Modeling, pp. 1–25. MIT Press, Cambridge (1998)

    Google Scholar 

  36. Kim, B., Hawes, S.L., Gillani, F., Wallace, L.J., Blackwell, K.T.: Signaling pathways involved in striatal synaptic plasticity are sensitive to temporal pattern and exhibit spatial specificity. PLoS Comput. Biol. 9(3), e1002953 (2013)

    CrossRef  Google Scholar 

  37. De Young, G.W., Keizer, J.: A single-pool inositol 1,4,5-trisphosphate-receptor-based model for agonist-stimulated oscillations in Ca2+ concentration. Proc. Natl. Acad. Sci. U.S.A. 89(20), 9895–9899 (1992)

    CrossRef  Google Scholar 

  38. Li, Y.X., Rinzel, J.: Equations for InsP3 receptor-mediated [Ca2+]i oscillations derived from a detailed kinetic model: a Hodgkin-Huxley like formalism. J. Theor. Biol. 166(4), 461–473 (1994)

    CrossRef  Google Scholar 

  39. Wade, J., McDaid, L., Harkin, J., Crunelli, V., Kelso, S.: Self-repair in a bidirectionally coupled astrocyte-neuron (AN) system based on retrograde signaling. Front. Comput. Neurosci. 6, 76 (2012)

    CrossRef  Google Scholar 

  40. Nadkarni, S., Jung, P.: Spontaneous oscillations of dressed neurons: a new mechanism for epilepsy? Phys. Rev. Lett. 91(26), 268101 (2003)

    CrossRef  Google Scholar 

  41. Zenke, F., Agnes, E.J., Gerstner, W.: Diverse synaptic plasticity mechanisms orchestrated to form and retrieve memories in spiking neural networks. Nature Commun. 6, 6922 (2015)

    CrossRef  Google Scholar 

  42. Furber, S.: Large-scale neuromorphic computing systems. J. Neural Eng. 13(5), 051001 (2016)

    CrossRef  Google Scholar 

Download references

Acknowledgments

The authors wish to thank Tampere University of Technology Graduate School, Emil Aaltonen Foundation, The Finnish Concordia Fund, and Ulla Tuominen Foundation for support for RH.

Funding

Funding.

This project received funding from the European Union Seventh Framework Programme (FP7) under grant agreement No. 604102 (HBP), European Union’s Horizon 2020 research and innovation programme under grant agreement No. 720270, and Academy of Finland (decision No. 297893).

Author information

Authors and Affiliations

  1. Computational Neuroscience Group, BioMediTech Institute and Faculty of Biomedical Sciences and Engineering, Tampere University of Technology, Tampere, Finland

    Riikka Havela, Tiina Manninen & Marja-Leena Linne

  2. Neuroscience Institute, Lithuanian University of Health Sciences, Kaunas, Lithuania

    Ausra Saudargiene

  3. Department of Informatics, Vytautas Magnus University, Kaunas, Lithuania

    Ausra Saudargiene

Authors
  1. Riikka Havela
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tiina Manninen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ausra Saudargiene
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Marja-Leena Linne
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Marja-Leena Linne .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Universidad Distrital Francisco José de Caldas, Bogotá, Colombia

    Juan Carlos Figueroa-García

Rights and permissions

Reprints and Permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Havela, R., Manninen, T., Saudargiene, A., Linne, ML. (2017). Modeling Neuron-Astrocyte Interactions: Towards Understanding Synaptic Plasticity and Learning in the Brain. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-63312-1_14

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63311-4

  • Online ISBN: 978-3-319-63312-1

  • eBook Packages: Computer ScienceComputer Science (R0)