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This article is part of the supplement: Sixteenth Annual Computational Neuroscience Meeting: CNS*2007

Open Access Oral presentation

Dense gap-junction connections support dynamic Turing structures in the cortex

D Alistair Steyn-Ross1*, Moira Steyn-Ross1, Marcus Wilson1 and Jamie Sleigh2

Author Affiliations

1 Department of Engineering, University of Waikato, Hamilton 3240, New Zealand

2 Waikato Clinical School, University of Auckland, Hamilton 3204, New Zealand

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BMC Neuroscience 2007, 8(Suppl 2):S2  doi:10.1186/1471-2202-8-S2-S2

The electronic version of this article is the complete one and can be found online at:

Published:6 July 2007

© 2007 Steyn-Ross et al; licensee BioMed Central Ltd.

Oral presentation

The recent report by Fukuda et al [1] provides convincing evidence for dense gap-junction connectivity between inhibitory neurons in the cat visual cortex, each neuron making 60 +/- 12 gap-junction dendritic connections with neurons in both the same and adjoining orientation columns. These resistive connections provide a source of diffusive current to the receiving neuron, supplementing the chemical-synaptic currents generated by incoming action-potential spike activity. Fukuda et al describe how the gap junctions form a dense and homogeneous electrical coupling of interneurons, and propose that this diffusion-coupled network provides the substrate for synchronization of neuronal populations.

To date, large-scale population-based mathematical models of the cortex have ignored diffusive communication between neurons. Here we augment a well-established mean-field cortical model [2] by incorporating gap-junction-mediated diffusion currents, and we investigate the implications of strong diffusive coupling. The significant result is the model prediction that the 2D cortex can spontaneously generate centimetre-scale Turing structures (spatial patterns), in which regions of high-firing activity are intermixed with regions of low-firing activity (see Fig. 1). Since coupling strength decreases with increases in firing rate, these patterns are expected to exchange contrast on a slow time-scale, with low-firing patches increasing their activity at the expense of high-firing patches. These theoretical predictions are consistent with the slowly fluctuating large-scale brain-activity images detected from the BOLD (blood oxygen-level-dependent) signal [3].

thumbnailFigure 1. Diffusion-induced Turing patterns in a square cortex of side 25 cm. Panel a shows the case of zero diffusion: the cortex organizes into a diffuse, cloud-like pattern, but fails to generate a Turing structure. Panels b-d show increasing inhibitory diffusion. These cases evolve into stable serpentine Turing patterns containing alternating regions of low-(blue) and high-firing (red) cells.


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    J Neurosci 2006, 26:3434-3443. PubMed Abstract | Publisher Full Text OpenURL

  2. Steyn-Ross DA, Steyn-Ross ML, Sleigh JW, Wilson MT, Gillies IP, Wright JJ: The sleep cycle modelled as a cortical phase transition.

    J Biol Phys 2005, 31:547-569. Publisher Full Text OpenURL

  3. Fox MD, Snyder AZ, Vincent JL, Corbetta M, van Essen DC, Raichle ME: The human brain is intrinsically organized into dynamic, anticorrelated functional networks.

    Proc Nat Acad Sci USA 102:9673-9678. PubMed Abstract | Publisher Full Text | PubMed Central Full Text OpenURL