Diversity of Cortical States at nonequilibrium Simulated by the Anti-Ferromagnetic Ising Model under Metropolis Dynamics

This article investigates the relationship between the interconnectivity and simulated dynamics of the thalamocortical system from the specific perspective of attempting to maximize the diversity of cortical states. This is achieved by designing the dynamics such that they favor opposing activity between adjacent regions, thus promoting dynamic diversity while avoiding widespread activation or de-activation. The anti-ferromagnetic Ising model with Metropolis dynamics is adopted and applied to four variations of the large-scale connectivity of the cat thalamocortical system: (a) considering only cortical regions and connections; (b) considering the entire thalamocortical system; (c) the same as in (b) but with the thalamic connections rewired so as to maintain the statistics of node degree and node degree correlations; and (d) as in (b) but with attenuated weights of the connections between cortical and thalamic nodes. A series of interesting findings are obtained, including the identification of specific substructures revealed by correlations between the activity of adjacent regions in case (a) and a pronounced effect of the thalamic connections in splitting the thalamocortical regions into two large groups of nearly homogenous opposite activation (i.e. cortical regions and thalamic nuclei, respectively) in cases (b) and (c). The latter effect is due to the existence of dense connections between cortical and thalamic regions and the lack of interconnectivity between the latter. Another interesting result regarding case (d) is the fact that the pattern of thalamic correlations tend to mirror that of the cortical regions. The possibility to control the level of correlation between the cortical regions by varying the strength of thalamocortical connections is also identified and discussed.

[1]  Albert-László Barabási,et al.  Statistical mechanics of complex networks , 2001, ArXiv.

[2]  Carlos A. Ruggiero,et al.  Complex grid computing , 2003, cond-mat/0312603.

[3]  Prof. Dr. Dr. Valentino Braitenberg,et al.  Cortex: Statistics and Geometry of Neuronal Connectivity , 1998, Springer Berlin Heidelberg.

[4]  Jun Ohkubo,et al.  Statistical-mechanical iterative algorithms on complex networks. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[5]  Gesine Reinert,et al.  Small worlds , 2001, Random Struct. Algorithms.

[6]  Luciano da Fontoura Costa,et al.  Correlating thalamocortical connectivity and activity , 2006 .

[7]  O. Sporns,et al.  Hierarchical features of large-scale cortical connectivity , 2005, q-bio/0508007.

[8]  Pablo M. Gleiser,et al.  Community Structure in Jazz , 2003, Adv. Complex Syst..

[9]  M. A. O'Neil,et al.  The connectional organization of the cortico-thalamic system of the cat. , 1999, Cerebral cortex.

[10]  Gerard T. Barkema,et al.  Monte Carlo Methods in Statistical Physics , 1999 .

[11]  Sergey N. Dorogovtsev,et al.  Correlations in interacting systems with a network topology , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  S. Brenner,et al.  The structure of the nervous system of the nematode Caenorhabditis elegans. , 1986, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[13]  Malcolm P. Young,et al.  Objective analysis of the topological organization of the primate cortical visual system , 1992, Nature.

[14]  Mark E. J. Newman,et al.  The Structure and Function of Complex Networks , 2003, SIAM Rev..

[15]  Hawoong Jeong,et al.  Random field Ising model and community structure in complex networks , 2005, cond-mat/0502672.

[16]  Luciano da Fontoura Costa,et al.  STRUCTURED THALAMOCORTICAL CONNECTIVITY REVEALED BY RANDOM WALKS ON COMPLEX NETWORKS , 2006, q-bio/0602021.

[17]  A. G. Williams,et al.  Scale-free networks in complex systems , 2006, SPIE Micro + Nano Materials, Devices, and Applications.

[18]  Olaf Sporns,et al.  The small world of the cerebral cortex , 2007, Neuroinformatics.

[19]  Alex Arenas,et al.  Synchronization reveals topological scales in complex networks. , 2006, Physical review letters.

[20]  O. Sporns,et al.  Motifs in Brain Networks , 2004, PLoS biology.

[21]  Amir Ayali,et al.  Morphological characterization of in vitro neuronal networks. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.