Skip to main content
SpringerLink
Log in

Self-influencing synaptic plasticity: Recurrent changes of synaptic weights can lead to specific functional properties

  • Published:
Journal of Computational Neuroscience Aims and scope Submit manuscript

An Erratum to this article was published on 27 February 2010

Abstract

Recent experimental results suggest that dendritic and back-propagating spikes can influence synaptic plasticity in different ways (Holthoff, 2004; Holthoff et al., 2005). In this study we investigate how these signals could interact at dendrites in space and time leading to changing plasticity properties at local synapse clusters. Similar to a previous study (Saudargiene et al., 2004) we employ a differential Hebbian learning rule to emulate spike-timing dependent plasticity and investigate how the interaction of dendritic and back-propagating spikes, as the post-synaptic signals, could influence plasticity. Specifically, we will show that local synaptic plasticity driven by spatially confined dendritic spikes can lead to the emergence of synaptic clusters with different properties. If one of these clusters can drive the neuron into spiking, plasticity may change and the now arising global influence of a back-propagating spike can lead to a further segregation of the clusters and possibly the dying-off of some of them leading to more functional specificity. These results suggest that through plasticity being a spatial and temporal local process, the computational properties of dendrites or complete neurons can be substantially augmented.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  • Abarbanel HDI, Gibb L, Huerta R, Rabinovich MI (2003) Biophysical model of synaptic plasticity dynamics. Biol. Cybern. 89(3): 214–226.

    Article  PubMed  Google Scholar 

  • Abarbanel HDI, Huerta R, Rabinovich MI (2002) Dynamical model of long-term synaptic plasticity. Proc. Natl. Acad. Sci. (USA) 99(15): 10132–10137.

    Google Scholar 

  • Bender VA, Feldman DE (2006) A dynamic spatial gradient of hebbian learning in dendrites. Neuron 51(2): 153–155 Commentary

    Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  • Bi GQ, Poo MM (1998) Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type. J. Neurosci. 18: 10464–10472.

    CAS  PubMed  Google Scholar 

  • Bliss TV, Gardner-Edwin AR (1973) Long-lasting potentiation of synaptic transmission in the dentate area of the unanaesthetized rabbit following stimulation of the perforant path. J. Physiol. (Lond.) 232: 357–374.

    CAS  Google Scholar 

  • Castellani GC, Quinlan EM, Cooper LN, Shouval HZ (2001) A biophysical model of bidirectional synaptic plasticity: dependence on AMPA and NMDA receptors. Proc. Natl. Acad. Sci. (USA) 98(22): 12772–12777.

    Google Scholar 

  • Eurich CW, Pawelzik K, Ernst U, Cowan JD, Milton JG (1999) Dynamics of self-organized delay adaptation. Phys. Rev. Lett. 82: 1594–1597.

    Article  CAS  Google Scholar 

  • Froemke RC, Dan Y (2002) Spike-timing-dependent synaptic modification induced by natural spike trains. Nature 416: 433–438.

    Article  CAS  PubMed  Google Scholar 

  • Froemke RC, Poo M-m, Dan Y (2005) Spike-timing-dependent synaptic plasticity depends on dendritic location. Nature 434: 221–225.

    Article  CAS  PubMed  Google Scholar 

  • Gasparini S, Migliore M, Magee JC (2004) On the initiation and propagation of dendritic spikes in CA1 pyramidal neurons. J. Neurosci. 24(49): 11046–11056.

    Article  CAS  PubMed  Google Scholar 

  • Golding N, Kath WL, Spruston N (2001) Dichotomy of action-potential backpropagation in ca1 pyramidal neuron dendrites. J Neurophysiol. 86: 2998–3010.

    CAS  PubMed  Google Scholar 

  • Golding NL, Spruston N (1998) Dendritic sodium spikes are variable triggers of axonal action potentials in hippocampal CA1 pyramidal neurons. Neuron 21: 1189–1200.

    Article  CAS  PubMed  Google Scholar 

  • Golding NL, Staff PN, Spurston N (2002) Dendritic spikes as a mechanism for cooperative long-term potentiation. Nature 418: 326–331.

    Article  CAS  PubMed  Google Scholar 

  • Govindarajan A, Kelleher RJ, Tonegawa S (2006) A clustered plasticity model of long-term memory engrams. Nature Rev. Neurosci. (Perspectives) 7: 575–583.

    Article  CAS  Google Scholar 

  • Hebb DO (1949) The Organization of Behavior: A Neurophychological Study. Wiley-Interscience, New York.

    Google Scholar 

  • Holthoff K (2004) Regenerative dendritic spikes and synaptic plasticity. Curr. Neurovasc. Res. 1(4): 381–387.

    Article  PubMed  Google Scholar 

  • Holthoff K, Kovalchuk Y, Yuste R, Konnerth A (2004) Single-shock LTD by local dendritic spikes in pyramidal neurons of mouse visual cortex. J. Physiol. 560.1: 27–36.

    Google Scholar 

  • Holthoff K, Kovalchuk Y, Yuste R, Konnerth A (2005) Single-shock plasticity induced by local dendritic spikes. In Proceedings of the Göttingen NWG Conference, p. 245B.

  • Karmarkar UR, Buonomano DV (2002) A model of spike-timing dependent plasticity: one or two coincidence detectors? J. Neurophysiol. 88: 507–513.

    Google Scholar 

  • Karmarkar UR, Najarian MT, Buonomano DV (2002) Mechanisms and significance of spike-timing dependent plasticity. Biol. Cybern. 87: 373–382.

    Article  PubMed  Google Scholar 

  • Kempter R, Gerstner W, van Hemmen JL (1999) Hebbian learning and spiking neurons. Phys. Rev. E. 59: 4498–4515.

    Article  CAS  Google Scholar 

  • Koch C (1999) Biophysics of Computation. Oxford University Press.

  • Larkum ME, Zhu JJ, Sakmann B (2001) Dendritic mechanisms underlying the coupling of the dendritic with the axonal action potential initiation zone of adult rat layer 5 pyramidal neurons. J. Physiol. (Lond.) 533: 447–466.

    Article  CAS  Google Scholar 

  • Letzkus JJ, Kampa BM, Stuart GJ (2006) Learning rules for spike timing-dependent plasticity depend on dendritic synapse location. J. Neurosci. 26(41): 10420–10429.

    Article  CAS  PubMed  Google Scholar 

  • Magee JC, Johnston D (1997) A synaptically controlled, associative signal for Hebbian plasticity in hippocampal neurons. Science 275: 209–213.

    Article  CAS  PubMed  Google Scholar 

  • Malenka RC, Nicoll RA (1999) Long-term potentiation-a decade of progress? Science 285: 1870–1874.

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  • Polsky A, Mel BW, Schiller J (2004) Computational subunits in thin dendrites of pyramidal cells. Nat Neurosci 7(6): 621–627.

    Article  CAS  PubMed  Google Scholar 

  • Porr B, Wörgötter F (2003) Isotropic sequence order learning. Neural Comp. 15: 831–864

    Article  Google Scholar 

  • Rubin JE, Gerkin RC, Bi GQ, C CC (2005) Calcium time course as a signal for spike-timing dependent plasticity. J. Neurophysiol. 0–0.

  • Saudargiene A, Porr B, Wörgötter F (2004) How the shape of pre- and postsynaptic signals can influence STDP: a biophysical model. Neural Comp. 16: 595–626.

    Article  Google Scholar 

  • Saudargiene A, Porr B, Wörgötter F (2005a) Local learning rules: predicted influence of dendritic location on synaptic modification in spike-timing-dependent plasticity. Biol. Cybern. 92: 128– 138.

    Article  Google Scholar 

  • Saudargiene A, Porr B, Wörgötter F (2005b) Synaptic modifications depend on synapse location and activity: a biophysical model of STDP. Biosystems 79: 3–10.

    Article  CAS  Google Scholar 

  • Senn W, Markram H, Tsodyks M (2000) An algorithm for modifying neurotransmitter release probability based on pre-and postsynaptic spike timing. Neural Comp. 13: 35–67.

    Article  Google Scholar 

  • Shouval HZ, Bear MF, Cooper LN (2002) A unified model of NMDA receptor-dependent bidirectional synaptic plasticity. Proc. Natl. Acad. Sci. (USA) 99(16): 10831–10836.

    Google Scholar 

  • Shouval HZ, Kalantziz G (2005) Stochastic properties of synaptic transmission affect the shape of spike time-dependent plasticity curves. J. Neurophysiol. 93: 1069–1073.

    Article  PubMed  Google Scholar 

  • Sjöström PJ, Häusser M (2006) A cooperative switch determines the sign of synaptic plasticity in distal dendrites of neocortical pyramidal neurons. Neuron 51(2): 227–238.

    Article  PubMed  Google Scholar 

  • Song S, Miller KD, Abbott LF (2000) Competitive hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neurosci. 3: 919–926.

    Article  CAS  PubMed  Google Scholar 

  • Stuart G, Spruston N, Sakmann B, Häusser M (1997) Action potential initiation and backpropagation in neurons of the mammalian central nervous system. Trends Neurosci. 20: 125–131.

    Article  CAS  PubMed  Google Scholar 

  • Swindale NV (1996) The development of topography in the visual cortex: a review of models. Network 7(2): 161–247.

    Article  CAS  PubMed  Google Scholar 

  • Wang H-X, C GR, Nauen DW, Bi G-Q (2005) Coactivation and timing-dependent integration of synaptic potentiation and depression. Nature Neurosci. 8: 187–193.

    Article  CAS  PubMed  Google Scholar 

  • Wespatat V, Tennigkeit F, Singer W (2004) Hebbian plasticity rules in fast oscillating visual cortical cells. In: FENS Forum Abstracts, FENS Lisbon, vol. 2, p. A031.2.

  • Williams SR, Stuart GJ (2003) Role of dendritic synapse location in the control of action potential output. TINS 26(3): 147–154.

    CAS  PubMed  Google Scholar 

  • Yeung LC, Shouval HZ, Blais BS, Cooper LN (2004) Synaptic homeostasis and input selectivity follow from a calcium-dependent plasticity model. Proc. Natl. Acad. Sci. 101: 14943–14948.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Psychology, University of Stirling, Stirling, FK9 4LA, Scotland

    Minija Tamosiunaite

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

    Minija Tamosiunaite

  3. Department of Electronics & Electrical Engineering, University of Glasgow, Glasgow, GT12 8LT, Scotland

    Bernd Porr

  4. Bernstein Center for Computational Neuroscience, University of Göttingen, Bunsenstr. 10 (at the MPI), D-37073, Göttingen, Germany

    Florentin Wörgötter

Authors
  1. Minija Tamosiunaite
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Bernd Porr
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Florentin Wörgötter
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Florentin Wörgötter.

Additional information

Action Editor: Wulfram Gerstner

An erratum to this article can be found online at http://dx.doi.org/10.1007/s10827-010-0218-7.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Tamosiunaite, M., Porr, B. & Wörgötter, F. Self-influencing synaptic plasticity: Recurrent changes of synaptic weights can lead to specific functional properties. J Comput Neurosci 23, 113–127 (2007). https://doi.org/10.1007/s10827-007-0021-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10827-007-0021-2

Keywords

Search

Navigation