Criticality of spreading dynamics in hierarchical cluster networks without inhibition

doi:10.1088/1367-2630/9/5/110

Criticality of spreading dynamics in hierarchical cluster networks without inhibition

Author

M Kaiser ^1,2,5, M Görner ³ and C C Hilgetag ⁴

Affiliations

¹ School of Computing Science, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
² Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
³ Department of Mathematics, UC Berkeley, Berkeley, CA 94720, USA
⁴ School of Engineering and Science, Jacobs University Bremen, Campus Ring 6, 28759 Bremen, Germany ,
⁵ Author to whom any correspondence should be addressed.

E-mail

M.Kaiser@ncl.ac.uk matthias@math.berkeley.edu c.hilgetag@iu-bremen.de

Journal

New Journal of Physics Create an alert RSS this journal

Issue

Volume 9, May 2007

Citation

M Kaiser et al 2007 New J. Phys. 9 110

doi: 10.1088/1367-2630/9/5/110

Article References Cited By

Tag this article Full text PDF (856 KB) View as HTML

Abstract

An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable network activations within a limited critical range. In this range, the activity of neural populations in the network persists between the extremes of quickly dying out, or activating the whole network. The nerve fibre network of the mammalian cerebral cortex possesses a modular organization extending across several levels of organization. Using a basic spreading model without inhibition, we investigated how functional activations of nodes propagate through such a hierarchically clustered network. The simulations demonstrated that persistent and scalable activations could be produced in clustered networks, but not in random networks of the same size. Moreover, the parameter range yielding critical activations was substantially larger in hierarchical cluster networks than in small-world networks of the same size. These findings indicate that a hierarchical cluster architecture may provide the structural basis for the stable and diverse functional patterns observed in cortical networks.

PACS

87.19.L- Neuroscience

87.18.Sn Neural networks and synaptic communication

Subjects