Erwan Plantec, Gautier Hamon, Mayalen Etcheverry, Bert Wang-Chak Chan, Pierre-Yves Oudeyer, Clément Moulin-Frier

Artificial Life (2025) 31 (2): 228–248.

Central to the Artificial Life endeavor is the creation of artificial systems that spontaneously generate properties found in the living world, such as autopoiesis, self-replication, evolution, and open-endedness. Though numerous models and paradigms have been proposed, cellular automata (CA) have taken a very important place in the field, notably because they enable the study of phenomena like self-reproduction and autopoiesis. Continuous CA like Lenia have been shown to produce lifelike patterns reminiscent, from both aesthetic and ontological points of view, of biological organisms we call “creatures.” We propose Flow-Lenia, a mass conservative extension of Lenia. We present experiments demonstrating its effectiveness in generating spatially localized patterns with complex behaviors and show that the update rule parameters can be optimized to generate complex creatures showing behaviors of interest. Furthermore, we show that Flow-Lenia allows us to embed the parameters of the model, defining the properties of the emerging patterns, within its own dynamics, thus allowing for multispecies simulation. Using the evolutionary activity framework and other metrics, we shed light on the emergent evolutionary dynamics taking place in this system.

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Lessons from developmental biology can be used to guide the behaviour of robot swarms.

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Andrea Santoro, Matteo Neri, Simone Poetto, Davide Orsenigo, Matteo Diano, Marilyn Gatica, Giovanni Petri

Traditional models of brain connectivity have primarily focused on pairwise interactions, over-looking the rich dynamics that emerge from simultaneous interactions among multiple brain regions. Although a plethora of higher-order interaction (HOI) metrics have been proposed, a systematic evaluation of their comparative properties and utility is missing. Here, we present the first large-scale analysis of information-theoretic and topological HOI metrics, applied to both resting-state and task fMRI data from 100 unrelated subjects of the Human Connectome Project. We identify a clear taxonomy of HOI metrics — redundant, synergistic, and topological—, with the latter acting as bridges along the redundancy–synergy continuum. Despite methodological differences, all HOI metrics align with the brain’s overarching unimodal-to-transmodal functional hierarchy. However, certain metrics show specific associations with the neurotransmitter receptor architecture. HOI metrics outperform traditional pairwise models in brain fingerprinting and perform comparably in task decoding, underscoring their value for characterizing individual functional profiles. Finally, multivariate analysis reveals that — among all HOI metrics — topological descriptors are key to linking brain function with behavioral variability, positioning them as valuable tools for linking neural architecture and cognitive function. Overall, our findings establish HOIs as a powerful framework for capturing the brain’s multidimensional dynamics, providing a conceptual map to guide their application across cognitive and clinical neuroscience.

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Anna Mancini, Balázs Lengyel, Riccardo Di Clemente, Giulio Cimini

Recent crises like the COVID-19 pandemic and geopolitical tensions have exposed vulnerabilities and caused disruptions of supply chains, leading to product shortages, increased costs, and economic instability. This has prompted increasing efforts to assess systemic risk, namely the effects of firm disruptions on entire economies. However, the ability of firms to react to crises by rewiring their supply links has been largely overlooked, limiting our understanding of production networks resilience. Here we study dynamics and determinants of firm-level systemic risk in the Hungarian production network from 2015 to 2022. We use as benchmark a heuristic maximum entropy null model that generates an ensemble of production networks at equilibrium, by preserving the total input (demand) and output (supply) of each firm at the sector level. We show that the fairly stable set of firms with highest systemic risk undergoes a structural change during COVID-19, as those enabling economic exchanges become key players in the economy — a result which is not reproduced by the null model. Although the empirical systemic risk aligns well with the null value until the onset of the pandemic, it becomes significantly smaller afterwards as the adaptive behavior of firms leads to a more resilient economy. Furthermore, firms’ international trade volume (being a subject of disruption) becomes a significant predictor of their systemic risk. However, international links cannot provide an unequivocal explanation for the observed trends, as imports and exports have opposing effects on local systemic risk through the supply and demand channels.

Read the full article at: arxiv.org