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Original Research ARTICLE

Front. Comput. Neurosci., 10 November 2011 | http://dx.doi.org/10.3389/fncom.2011.00047

Synaptic scaling in combination with many generic plasticity mechanisms stabilizes circuit connectivity

  • 1 Institute for Physics – Biophysics, Georg-August-University, Göttingen, Germany
  • 2 Network Dynamics Group, Max Planck Institute for Dynamics and Self-Organization, Göttingen, Germany
  • 3 Bernstein Center for Computational Neuroscience, Georg-August-University, Göttingen, Germany
  • 4 Institute for Physics – Non-linear Dynamics, Georg-August-University, Göttingen, Germany

Synaptic scaling is a slow process that modifies synapses, keeping the firing rate of neural circuits in specific regimes. Together with other processes, such as conventional synaptic plasticity in the form of long term depression and potentiation, synaptic scaling changes the synaptic patterns in a network, ensuring diverse, functionally relevant, stable, and input-dependent connectivity. How synaptic patterns are generated and stabilized, however, is largely unknown. Here we formally describe and analyze synaptic scaling based on results from experimental studies and demonstrate that the combination of different conventional plasticity mechanisms and synaptic scaling provides a powerful general framework for regulating network connectivity. In addition, we design several simple models that reproduce experimentally observed synaptic distributions as well as the observed synaptic modifications during sustained activity changes. These models predict that the combination of plasticity with scaling generates globally stable, input-controlled synaptic patterns, also in recurrent networks. Thus, in combination with other forms of plasticity, synaptic scaling can robustly yield neuronal circuits with high synaptic diversity, which potentially enables robust dynamic storage of complex activation patterns. This mechanism is even more pronounced when considering networks with a realistic degree of inhibition. Synaptic scaling combined with plasticity could thus be the basis for learning structured behavior even in initially random networks.

Keywords: plasticity, neural network, homeostasis, synapse

Citation: Tetzlaff C, Kolodziejski C, Timme M and Wörgötter F (2011) Synaptic scaling in combination with many generic plasticity mechanisms stabilizes circuit connectivity. Front. Comput. Neurosci. 5:47. doi: 10.3389/fncom.2011.00047

Received: 02 August 2011; Accepted: 20 October 2011;
Published online: 10 November 2011.

Edited by:

David Hansel, University of Paris, France

Reviewed by:

Germán Mato, Centro Atomico Bariloche, Argentina
Sandro Romani, Columbia University, USA

Copyright: © 2011 Tetzlaff, Kolodziejski, Timme and Wörgötter. This is an open-access article subject to a non-exclusive license between the authors and Frontiers Media SA, which permits use, distribution and reproduction in other forums, provided the original authors and source are credited and other Frontiers conditions are complied with.

*Correspondence: Christian Tetzlaff, Network Dynamics Groups, Max Planck Institute for Dynamics and Self-Organization, Bunsenstr. 10, 37073 Göttingen, Germany. e-mail: tetzlaff@physik3.gwdg.de