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  • Open Access

Switching to criticality by synchronized input

BMC Neuroscience200910 (Suppl 1) :P155

  • Published:


  • Network Effect
  • Granular Medium
  • Critical Interval
  • Realistic Dynamic
  • Synaptic Potentiation

The concept of self-organized criticality (SOC) describes a variety of phenomena ranging from plate tectonics, the dynamics of granular media and stick-slip motion to neural avalanches [1]. In all these cases the dynamics is marginally stable and event sizes obey a characteristic power-law distribution.

It was previously shown that an extended critical interval can be obtained in a neural network by incorporation of depressive synapses [2]. In the present study we scrutinize a more realistic dynamics for the synaptic interactions that can be considered as the state-of-the-art in computational modeling of synaptic interaction (Figure 1) [2]. Interestingly, the more complex model does not exclude an analytical treatment and it shows a type of stationary state consisting of self-organized critical phase and a subcritical phase that has not been previously described. The phases are connected by first- or second-order phase transitions in a cusp bifurcation. Switching between phases can be induced by synchronized activity or by activity deprivation (Figure 2). We present exact analytical results supported by extensive numerical simulations.
Figure 1
Figure 1

The distribution of avalanche sizes changes in dependence on the interaction parameter α from subcritical (circles, α = 0.52) via critical (stars, α = 0.56) to supercritical (triangles, α = 0.59). The inset shows the hysteresis of the distribution at an exemplary avalanche size (L = 40). The circles result at increasing α, stars at decreasing α.

Figure 2
Figure 2

Dynamic transition from a critical (red circles) to a subcritical state (green circles) by short-term activity deprivation. The two stars represent the immediate effect of the deprivation in a particular trial.

By elucidating the relation between the elementary synaptic processes and the network dynamics, our mean-field approach revealed a macroscopic bifurcation pattern, which can be verified experimentally through predicted hysteresis. Furthermore it may be able to explain observations of up and down states in the prefrontal cortex [4] as well as the discrete changes in synaptic potentiation and depression [5] as network effects.

Authors’ Affiliations

Bernstein Center for Computational Neuroscience, 37073 Göttingen, Germany
Max Planck Institute for Dynamics and Self-Organization, 37073 Göttingen, Germany
IPAB, School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK


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© Levina et al; licensee BioMed Central Ltd. 2009

This article is published under license to BioMed Central Ltd.