Embracing Complexity: Abstracts

Below, you will find the abstracts submitted for the workshop "Embracing Complexity: Principled and Practical Approaches to Emergence".

Beyond Reduction and Emergence: A Distributed View of Minds

Antonella Tramacere (Università Roma Tre, Italy)

This talk presents a distributed view of mental systems, challenging both reductionist and overly broad emergentist frameworks. Drawing on key cases from cognitive neuroscience, I propose a diachronic and integrative perspective where causal control over system behavior is dynamically distributed across internal and external components. Mental phenomena arise through continuous interactions across components and processes, dissolving strict ontic boundaries between levels and challenging distinctions between internal and external influences. Methodologically, this view encourages interdisciplinary approaches that incorporate temporal, environmental, and developmental dimensions of living systems, while also calling for criteria for defining what constitutes a mental system.

Advanced Methods in Nonlinear Time Series Analysis

Julia Mindlin (University of Leipzig, Germany)

This course provides an in-depth exploration of advanced techniques in nonlinear time series analysis. A core component of the course covers time delay embeddings, based on Takens’ theorem, for reconstructing phase space dynamics from scalar observations. We introduce methods for selecting optimal embedding parameters, and imparticular work with an implementation of the false neighbors method, ensuring minimal self-intersections in reconstructed attractors. To validate dynamical models that can reproduce the behaviour of the experimental time series data, we explore topological analysis of periodic orbits through linking and self-linking numbers (LN, SLN), a method for comparing model-generated and observed attractor structures. We then transition to complexity measures, introducing Shannon entropy, disequilibrium metrics, and the Bandt-Pompe permutation entropy framework to quantify information content and system disorder.

The course includes hands-on computational exercises in Python, emphasizing practical implementation of these methods using real-world application from the climate system. We will work examining the Niño-3.4 region's sea surface temperature (SST) variability using daily data reconstruction and spectral filtering methods. We will discuss how to infer appropiate sampling rates and delays for the time delay embedding based on this particular application. By the end, participants will be equipped with the theoretical foundations and coding skills to analyze nonlinear time series in climate and other complex systems. This will allow participants to approach more complex methods (i.e. machine learning) recently proposed to reconstruct phase spaces or find dynamical models from data. Finally, we will provide a short overview of relevant literature to further explore these analysis tools.

Together, but not the same: self-organisation towards emergent collectives

Madalina Sas (Imperial College London, UK)

We are living in a time of social alienation and political division, where many people feel disconnected from the leadership structures who fail to represent their interests; but in the natural world of eusocial insects and social animals, such inefficient social structures would not survive.

In my talk, I will try to show how the kind of self-organising behaviour seen in animals can be tremendously beneficial to humans. I will draw from studies of swarm intelligence in the natural world, starting with how birds flock, and ants vote, and slime moulds solve mazes, and discuss how these behaviours relate to behaviour in humans, by comparison with different frameworks in philosophy, anthropology, and sociology. Then I will present quantitative studies of self-organised behaviour in humans, and relate it to important collective activities such as rituals, protests and marches, art and sport, but also important modes of interaction to foster such collective activities, such as joint action and improvisation.

Finally, I present Synch.Live, a novel participatory experimental technology and collective artistic experience inspired from swarm intelligence. Its goal is to induce collective behaviour in human groups, study the conditions required for self-organisation to emerge, as well as the balance between individual and collective.

Large-scale integration of perceptual and predictive information is encoded by non-oscillatory neural dynamics

Andres Canales (University of Cambridge, UK)

 The brain is characterized by extensive recurrent connectivity within and between areas. This recurrent connectivity enables various patterns of arrhythmic (non-oscillatory) and rhythmic (oscillatory) neural activity that are temporally coordinated between regions. What role do these distinct dynamics play in the large-scale integration of perceptual and predictive information? In this talk, I will discuss how information theory combined with EEG , ECoG, and computational modelling can help us uncover large-scale neural patterns of non-oscillatory activity during perception and prediction. In the first series of studies, I will show how non-oscillatory rather than oscillatory dynamics encode perceptual and predictive information across sensory modalities in different species. In the second part, I will discuss how non-oscillatory dynamics encode synergistic (complementary) rather than redundant (common) information between brain areas during visual and auditory predictive processing. These empirical and theoretical observations will provide new insights into the functional role of non-oscillatory dynamics during the large-scale integration of perceptual and predictive information.