Ramiro Plüss | Physicist and PhD(c)

Physicist (UNR)

PhD(c) (ITBA)

CABA, Argentina

Ramiro Plüss holds a degree in Physics from the National University of Rosario (UNR, FCEIA) and is currently a PhD candidate in Engineering at the Buenos Aires Institute of Technology (ITBA). His research focuses on the bidirectional relationship between structure, neural dynamics, and behavior, examining how anatomical organization shapes neural activity and motor control, and how neural activity and behavior, in turn, shape structure. He combines computational neuroscience, neural network models, and bioinspired robotic systems to study proprioception and embodied motor control.

Schematic overview linking connectome structure, neural dynamics, embodiment, and behavior.

In previous work, he studied how changes in connection density in adaptive networks affect collective dynamics and network organization, including integration and segregation. He has also applied dynamical network models to human connectome data from control subjects and patients with schizophrenia to improve the modeling of functional connectivity.

Currently, he is working with robotic models driven by real connectomics data from Drosophila and C. elegans to explore embodiment, autonomy, and sensorimotor feedback. These connectome based architectures can be viewed as biologically pre trained networks, offering a bridge between neuroscience and robotics while reducing reliance on conventional artificial training. Through this line of research, he aims to contribute to autonomous and bioinspired robotics, long range exploration, and broader questions about how structure, dynamics, and embodied interaction give rise to behavior and intelligence in both biological and artificial systems.

Selected Publications

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.
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.