Volume 8 Supplement 2

Sixteenth Annual Computational Neuroscience Meeting: CNS*2007

Open Access

A model for correlation detection based on Ca2+concentration in spines

  • Moritz Helias1Email author,
  • Stefan Rotter1, 3,
  • Marc-Oliver Gewaltig1, 4 and
  • Markus Diesmann1, 2
BMC Neuroscience20078(Suppl 2):P192

DOI: 10.1186/1471-2202-8-S2-P192

Published: 6 July 2007

Understanding the mechanisms of correlation detection between pre- and postsynaptic activity at a synapse is crucial for the theory of Hebbian learning and development [1, 2] of cortical networks. The calcium concentration in spines was experimentally shown to be a correlation sensitive signal confined to the spine: A supralinear influx of calcium into spines occurs when presynaptic stimulation precedes a backpropagating action potential within a short time window. The magnitude of the influx depends on the relative timing tpost-tpre [3]. There is strong evidence that NMDA (N-methyl d-aspartate) receptors are responsible for the supralinear effect [3]. Previous simulation studies relate the occurrence of spike time dependent plasticity to this calcium signal [4, 5]. However, these simulations mainly focus on pairs and triplets of pre- and postsynaptic spikes, rather than on irregular activity. Here, we investigate the properties of a biologically motivated model for correlation detection based on the calcium influx through NMDA receptors under realistic conditions of irregular pre- and postsynaptic spike trains with weak correlation. We demonstrate that a simple thresholding mechanism acts as a sensitive correlation detector robustly operating at physiological firing rates. We identify the regime (rate, correlation coefficient, detection time) in which this mechanism can assess the correlation between pre- and postsynaptic activity. Furthermore, we show that correlation controlled synaptic pruning acts as a mechanism of homeostasis, and that cooperation between synapses leads to a connectivity structure reflecting the spatial correlations in the input. The detector model allows for a computationally effective implementation usable in large-scale network simulations. On the single synapse level most of the results are confirmed by an analytical model.

Declarations

Acknowledgements

Partially funded by DIP F1.2, BMBF Grant 01GQ0420 to the Bernstein Center for Computational Neuroscience Freiburg and EU Grant 15879 (FACETS).

Authors’ Affiliations

(1)
Bernstein Center for Computational Neuroscience (BCCN), Albert-Ludwigs-University Freiburg
(2)
Computational Neuroscience Group, RIKEN Brain Science Institute
(3)
Institute for Frontier Areas of Psychology and Mental Health
(4)
Honda Research Institute Europe GmbH

References

  1. Le Be JV, Markram H: Spontaneous and evoked synaptic rewiring in the neonatal neo-cortex. Proc Natl Acad Sci USA. 2006, 103 (35): 13214-13219. 10.1073/pnas.0604691103.PubMedPubMed CentralView ArticleGoogle Scholar
  2. Rumpel S, Hatt H, Gottmann K: Silent synapses in the developing rat visual cortex: Evidence for postsynaptic expression of synaptic plasticity. J Neurosci. 1998, 18 (21): 8863-8874.PubMedGoogle Scholar
  3. Nevian T, Sakmann B: Single spine Ca2+ signals evoked by coincident epsps and backpropagating action potentials in spiny stellate cells of layer 4 in the juvenile rat somatosensory barrel cortex. J Neurosci. 2004, 24 (7): 1689-1699. 10.1523/JNEUROSCI.3332-03.2004.PubMedView ArticleGoogle Scholar
  4. Rubin JE, Gerkin RC, Bi G-Q, Chow CC: Calcium time course as a signal for spike-timing-dependent plasticity. J Neurophysiol. 2005, 93: 2600-2613. 10.1152/jn.00803.2004.PubMedView ArticleGoogle Scholar
  5. Saudargine A, Porr B, Worgotter F: Local learning rules: predicted influence of dendritic location on synaptic modification in spike-timing-dependent plasticity. Biol Cybern. 2005, 92 (2): 128-138. 10.1007/s00422-004-0525-z.View ArticleGoogle Scholar

Copyright

© Helias et al; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd.

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