Diversity of Cortical States at Non-Equilibrium Simulated by the Ferromagnetic Ising Model Under Metropolis Dynamics

TL;DR

This study uses an anti-ferromagnetic Ising model with Metropolis dynamics to explore how different connectivity structures in the thalamocortical system influence the diversity of cortical states, revealing structural effects on activity patterns.

Contribution

It introduces a novel application of the Ising model to simulate and analyze the impact of connectivity variations on cortical state diversity in the thalamocortical system.

Findings

Thalamic connections split regions into two groups with opposite activity.

Connectivity structure influences correlation patterns between regions.

Varying connection strength controls cortical correlation levels.

Abstract

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 tended 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.