Connectionists: Papers: balanced network activity without inhibition; network reconstruction from local features; simulated robustness of cortical networks

[Marcus Kaiser]

Dear colleagues,

I want to advertise our following three papers that deal with 

(1) how network topology alone can lead to balanced network activity that neither dies out nor spreads through the whole network. This model does not involve inhibitory nodes and might thus be applicable to the global network level of connections between columns, areas, and area clusters.

(2) how to reconstruct cortical networks from local features of the individual nodes. These include both topological features as well as spatial features (e.g., the metric distance between nodes). Whereas we show the application to structural networks, the approach might be useful for reconstructing functional networks from incomplete data.

(3) simulations that show that the cortical network structure is robust against random elimination of nodes or edges but quickly falls apart for targeted attack of critical components. This feature is shared with benchmark scale-free networks but not with small-world or rewired benchmark networks. This structural robustness against lesions might underlie the varying extent of functional deficits in lesion patients.

Abstracts and links to the articles are:


(1) Criticality of spreading dynamics in hierarchical cluster networks without inhibition Marcus Kaiser, Matthias Görner, Claus C Hilgetag New Journal of Physics 9:110, May 2007

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.
http://stacks.iop.org/1367-2630/9/110



(2) Predicting the connectivity of primate cortical networks from topological and spatial node properties Luciano da F Costa, Marcus Kaiser, Claus C. Hilgetag BMC Systems Biology 1:16, 8 March 2007

The organization of the connectivity between mammalian cortical areas has become a major subject of study, because of its important role in scaffolding the macroscopic aspects of animal behavior and intelligence. In this study we present a computational reconstruction approach to the problem of network organization, by considering the topological and spatial features of each area in the primate cerebral cortex as subsidy for the reconstruction of the global cortical network connectivity. Starting with all areas being disconnected, pairs of areas with similar sets of features are linked together, in an attempt to recover the original network structure.  Inferring primate cortical connectivity from the properties of the nodes, remarkably good reconstructions of the global network organization could be obtained, with the topological features allowing slightly superior accuracy to the spatial ones. Analogous reconstruction attempts for the C. elegans neuronal network resulted in!
  substantially poorer recovery, indicating that cortical area interconnections are relatively stronger related to the considered topological and spatial properties than neuronal projections in the nematode.  The close relationship between area-based features and global connectivity may hint on developmental rules and constraints for cortical networks. Particularly, differences between the predictions from topological and spatial properties, together with the poorer recovery resulting from spatial properties, indicate that the organization of cortical networks is not entirely determined by spatial constraints.
http://www.biomedcentral.com/1752-0509/1/16/abstract



(3) Simulation of Robustness against Lesions of Cortical Networks Marcus Kaiser, Robert Martin, Peter Andras, Malcolm P. Young European Journal of Neuroscience 25:3185-3192, May 2007 

Structure entails function and thus a structural description of the brain will help to understand its function and may provide insights into many properties of brain systems, from their robustness and recovery from damage, to their dynamics and even their evolution. Advances in the analysis of complex networks provide useful new approaches to understanding structural and functional properties of brain networks. Structural properties of networks recently described allow their characterization as small-world, random (exponential) and scale-free. They complement the set of other properties that have been explored in the context of brain connectivity, such as topology, hodology, clustering, and hierarchical organization. Here we apply new network analysis methods to cortical inter-areal connectivity networks for the cat and macaque brains. We compare these corticocortical fibre networks to benchmark rewired, small-world, scale-free and random networks, using two analysis strateg!
 ies, in which we measure the effects of the removal of nodes and connections on the structural properties of the cortical networks. The brain networks' structural decay is in most respects similar to that of scale-free networks. The results implicate highly connected hub-nodes and bottleneck connections as structural basis for some of the conditional robustness of brain systems. This informs the understanding of the development of brain networks' connectivity. 
http://arxiv.org/abs/0704.0392v1


Regards,
 Marcus

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Marcus Kaiser, Ph.D.
RCUK Academic Fellow
School of Computing Science
Newcastle University
Claremont Tower
Newcastle upon Tyne NE1 7RU, U.K.
Phone: +44 191 222 8161
Fax:   +44 191 222 8232
http://www.biological-networks.org/