Publications

2026

1. CCIS

   

   Hemispheric-Specific Coupling Improves Modeling of Functional Connectivity Using Wilson–Cowan Dynamics

   Ramiro Plüss, Hernán Villota, and Patricio Orio

   In Computational Neuroscience, Communications in Computer and Information Science (CCIS, Springer Nature), vol. 2734 , 2026

   Latin American Workshop on Computational Neuroscience (LAWCN, 2025)

   Abs DOI arXiv ITBA Repository

   Large-scale neural mass models have been widely used to simulate resting-state brain activity from structural connectivity. In this work, we extend a well-established Wilson-Cowan framework by introducing a novel hemispheric-specific coupling scheme that differentiates between intra-hemispheric and inter-hemispheric structural interactions. We apply this model to empirical cortical connectomes and resting-state fMRI data from matched control and schizophrenia groups. Simulated functional connectivity is computed from the band-limited envelope correlations of regional excitatory activity and compared against empirical functional connectivity matrices. Our results show that incorporating hemispheric asymmetries enhances the correlation between simulated and empirical functional connectivity, highlighting the importance of anatomically informed coupling strategies in improving the biological realism of large-scale brain network models.

2025

1. arXiv

   

   The Role of Connection Density in Adaptive Networks with Chaotic Units

   Ramiro Plüss and Pablo Gleiser

   2025

   Abs arXiv ITBA Repository

   

   We investigate the role of connection density in an adaptive network model of chaotic units that dynamically rewire based on their internal states and local coherence. By systematically varying the network’s connectivity density, we uncover distinct dynamical regimes and structural transitions, revealing mechanisms of spontaneous modularity, dynamical segregation, and integration. We find that at higher densities, the network exhibits both local clustering and global synchrony. Additionally, we observe that low-density networks tend to fragment into desynchronized clusters, while high-density networks converge to synchronized states combining strong global integration with persistent modular segregation. Inspired by neural architectures, this model provides a general framework for understanding how simple microscopic rules can give rise to complex emergent behaviors in dynamical networks.

2023

1. RePHip

   

   Bachelor’s thesis in Physics: “Dynamics and Structure in Adaptive Neural Networks”

   Ramiro Plüss

   National University of Rosario (UNR), Faculty of Exact Sciences, Engineering and Surveying (FCEIA) , 2023

   Abs UNR RePHip repository

   A connectome is a map of the connections between neurons in the brain, and the production and study of connectomes is known as connectomics. This can be modeled using an adaptive neural network to analyze the relationship between network structure and node dynamics. The microscopic units of the model are dynamical nodes representing neuronal activity, whose interactions give rise to complex network structures. Links between nodes are selected through an adaptive algorithm that connects dynamical elements with similar internal states. The objective of this work was to study the temporal evolution of the network across different phases while varying parameters such as the coupling factor between nodes and the average number of links per node, obtaining an original result based on the model of Gong and Van Leeuwen. Their work was motivated by the need to understand how biological systems, such as neural networks, can evolve toward a small-world network structure even when their individual components (i.e., nodes) exhibit chaotic dynamics. It has important implications for understanding how biological systems can develop small-world organization and how neural networks may remain efficient at information processing despite chaotic dynamics at the level of individual neurons. To address this question, Gong and Van Leeuwen developed a mathematical model of a neural network with chaotic nodes and subjected the network to a selection process based on its ability to process information. They found that, over time, the network evolved toward a small-world structure, allowing information to propagate efficiently despite the chaotic dynamics of individual nodes. To achieve the objectives of this thesis, numerical simulations were carried out implementing both the individual node dynamics and their interactions through the network, together with functions and parameters used to quantify the network state. The initial goal was to reproduce results reported in previous studies using chaotic maps and specific dynamical mechanisms. Once these results were replicated, the focus shifted to a deeper analysis of a phase transition identified by Gong and Van Leeuwen, incorporating new parameters and additional analyses to characterize the two existing phases as well as the critical transition point.