Optimal Hierarchical Modular Topologies for Producing Limited Sustained Activation of Neural Networks

By Marcus Kaiser, Claus C. Hilgetag. Front. Neuroinform., 2009

334 citations recorded by the source corpus.

An essential requirement for the representation of functional patterns in complex neural networks, such as the mammalian cerebral cortex, is the existence of stable regimes of network activation, typically arising from a limited parameter range. In this range of limited sustained activity (LSA), the activity of neural populations in the network persists between the extremes of either quickly dying out or activating the whole network. Hierarchical modular networks were previously found to show a wider parameter range for LSA than random or small-world networks not possessing hierarchical organization or multiple modules. Here we explored how variation in the number of hierarchical levels and modules per level influenced network dynamics and occurrence of LSA. We tested hierarchical configurations of different network sizes, approximating the large-scale networks linking cortical columns in one hemisphere of the rat, cat, or macaque monkey brain. Scaling of the network size affected the number of hierarchical levels and modules in the optimal networks, also depending on whether global edge density or the numbers of connections per node were kept constant. For constant edge density, only few network configurations, possessing an intermediate number of levels and a large number of modules, led to a large range of LSA independent of brain size. For a constant number of node connections, there was a trend for optimal configurations in larger-size networks to possess a larger number of hierarchical levels or more modules. These results may help to explain the trend to greater network complexity apparent in larger brains and may indicate that this complexity is required for maintaining stable levels of neural activation.

Subfields: Cognitive Neuroscience, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a839aad2124adddf829625fdcf9613334ca9d1f0

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Citations in the corpus

Co-activation patterns distinguish cortical modules, their connectivity and functional differentiation (2011, methodology): “In this paper, we propose a set of neuroinformatic tools to investigate a given seed region's structural–connectional and functional properties.”
Modular and Hierarchically Modular Organization of Brain Networks (2010, background): “The presence of modules allows some neuronal activity to remain locally encapsulated and to maintain dynamical balance (Kaiser et al., 2007; Kaiser and Hilgetag, 2010), i.e., dynamical activity is maintained between the extremes of rapidly dying out and invading the whole network.”
A review of modularization techniques in artificial neural networks (2019, background): “Even for theoretically focused reviews, the taxonomy is sparse and fails to capture important properties and abstractions (Ronco and Gawthrop 1995; Auda and Kamel 1999; Sharkey 1996; Kacprzyk and Pedrycz 2015).”
Cerebellar supervised learning revisited: biophysical modeling and degrees-of-freedom control. (2011, methodology): “Wils S, De Schutter E: STEPS: modeling and simulating complex reaction-diffusion systems with Python.”
Connectivity profiles reveal the relationship between brain areas for social cognition in human and monkey temporoparietal cortex (2013, methodology): “Therefore, to explore the relationship between human areas involved in processing faces and in ToM, we performed two activation likelihood estimation (ALE) metaanalyses of human neuroimaging data using the BrainMap database (27).”
Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder (2010, methodology): “One approach that has provided support for the validity of the ensuing networks [Smith et al., 2009] is the delineation of task-related functional connectivity networks using coordinate-based meta-analysis [Eickhoff et al., 2009a; Laird et al., 2009].”