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  • Poster presentation
  • Open Access

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

  • 1Email author,
  • 1, 3,
  • 1, 4 and
  • 1, 2
BMC Neuroscience20078 (Suppl 2) :P192

  • Published:


  • Spike Train
  • Hebbian Learning
  • Short Time Window
  • Postsynaptic Activity
  • Dependent Plasticity

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.



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

Authors’ Affiliations

Bernstein Center for Computational Neuroscience (BCCN), Albert-Ludwigs-University Freiburg, Germany
Computational Neuroscience Group, RIKEN Brain Science Institute, Wako, Japan
Institute for Frontier Areas of Psychology and Mental Health, Freiburg, Germany
Honda Research Institute Europe GmbH, Offenbach, Germany


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© Helias et al; licensee BioMed Central Ltd. 2007

This article is published under license to BioMed Central Ltd.