>> DIEP seminars and talks - 2025
DIEP is a broad interdisciplinary endeavour with the aim of bringing scientists together and fostering collaborations in order to progress the science of emergence. For this purpose, DIEP organises a wide variety of events, ranging from brainstorming sessions, community building events, workshops, conferences, public lectures, technical talks and cocktail parties. Regular events include workshops and DIEP seminars (see also the events page).
To register for the DIEP seminars taking place every Thursday please fill out the form here. See full calendar of events here.
See recordings of all talks here. To access older seminars you can check the seminar archives of 2018/2019, 2021, 2022, 2023, and 2024.
>> DIEP Seminar: Alexandre René (RWTH Aachen)
Truth in the Age of Deep Learning | 11am, 6th of November 2025
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Fitting models to data is an important part of the practice of science. Ongoing advances in machine learning have made it possible to fit more - and more complex - models, but have also exacerbated a problem: when multiple models fit the data equally well, which one(s) should we pick? The answer depends entirely on the modelling goal. In the scientific context, the essential goal is replicability: if a model works well to describe one experiment, it should continue to do so when that experiment is replicated tomorrow, or in another laboratory. The selection criterion must therefore be robust to the variations inherent to the replication process. In this work we develop a nonparametric method for estimating uncertainty on a model's empirical risk when replications are non-stationary, thus ensuring that a model is only rejected when another is reproducibly better. I will present a solution to this problem which comes in two parts. First I provide an ontological argument, which reframes the model selection problem such as to account for uncertainty in replications. I will provide a brief survey of the most common statistical methods and explain how they fall short in this context. In the second part, I then use some linearity assumptions on the quantile functions of the loss, to construct a practical computation procedure for the desired uncertainty. Along the way, I will discuss the peculiarities of defining stochastic processes over quantile functions, and how they led us to propose the "hierarchical beta process". I will illustrate the method in two settings. One is a pedagogical example comparing the Rayleigh-Jeans and Planck models for the spectral radiance. The other is closer to the machine learning setting, comparing different model candidates for biological neurons which differ only in the value of their parameters.
>> DIEP Seminar: Ben Martin (University of Amsterdam)
Towards a Mechanics of Predation and Escape | 11am, 30th of October 2025
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Predation is among the most fundamental processes shaping ecological and evolutionary dynamics, yet the underlying mechanics of how predation unfolds remain poorly understood. What determines whether prey successfully evade or are captured? Which traits of predators and prey matter most? And how do both parties transform streams of sensory information into rapid behavioral decisions? In this talk, I will present work combining theory, laboratory experiments, and field observations to develop a general mechanistic framework for predator-prey interactions. Although animal behavior is often viewed as complex and idiosyncratic, the dynamics of pursuit and escape can be captured with surprising fidelity by simple mathematical models. Extending these models across body sizes and environments reveals clear allometric structure in pursuit dynamics, giving rise to distinct biomechanical regimes in which different traits-such as speed, maneuverability, or sensory-motor delays-govern success or failure. These findings hold great promise for advancing a general theory of predator-prey interactions, capable of predicting who eats whom, and at what rates, across a broad range of ecological systems.
>> DIEP Seminar: Sebastien Lion (Centre d'Ecologie Fonctionnelle et Evolutive, Montpellier)
Coupling natural selection and epidemiology | 11am, 23th of October 2025
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The COVID-19 pandemic has demonstrated that pathogen evolution can be very rapid, leading to potentially complex interactions between evolutionary and epidemiological dynamics. In this talk, Sebastien will discuss how theoretical evolutionary ecology can be used to understand the short- and long-term evolutionary epidemiology of pathogens. First, he will present conceptual models clarifying how pathogens can evolve in response to public health interventions such as vaccination, both on short and long time scales. Second, Sebastien will discuss a methodological framework that can be used to model evolution at different time scales and better capture the dynamic feedback between epidemiology and evolution. Overall, this talk aims to demonstrate how approaches such as quantitative genetics and adaptive dynamics, and concepts such as reproductive value and fitness, can be brought together to shed light on conceptual and applied problems in evolutionary epidemiology.
>> DIEP Seminar: Ben Meylahn (University of Amsterdam)
Multi-agent learning models for social dynamics | 11am, 16th of October 2025
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There are many models for social dynamics. Some of these are clean and simple, others can be overly complex. With multi-agent learning Ben Meylahn tries to cut a balance: Agents have only one or two parameters, while still being sophisticated enough to learn from experience. Ben will illustrate this approach by discussing two models for trust dynamics, and one model for opinion dynamics.
>> DIEP Seminar: Deepak Gupta (TU Berlin)
Control of Nonequilibrium Systems | 11am, 9th of October 2025
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Designing low-dissipation control driving protocols (optimal protocols) for small-scale systems (length-scale: ~nanometer—micrometer), where thermal fluctuations play a significant role, is an active field of research. This field has attracted researchers from various scientific disciplines, including Physics, Chemistry, Biology, and Mathematics. A driving protocol refers to a procedure by which a system is driven from one configuration in its state space to another. For example, unzipping a DNA hairpin from a zippedconfiguration or vice versa . In this talk, Deepak will specifically discuss designing efficient driving procedures for two different systems: 1) A biomolecular motor—the F1ATPase, and 2) an active Ornstein-Uhlenbeck particle. In general, designing such protocols is challenging due to the spatial nonlinearity of the systems and the presence of environmental thermal fluctuations. Nonetheless, a near-equilibrium (linear response ) framework is found to apply to a broad class of small-scale systems. He will follow this framework to design non-trivial protocols to drive the F1's γ-shaft to synthesize ATP at low-dissipation cost. Further, this near-equilibrium framework5 will be applied to construct the driving protocols for an active Ornstein-Uhlenbeck particle to drive this particle over a non-linear spatial potential energy landscape. (Notice that this particle is in a non-equilibrium stationary state at a fixed control parameter.) For both cases, the analysis reveals that the designed protocols, based on the linear response (or close-to-equilibrium) approach, dissipate lower energy as compared to the constant velocity driving protocol for a wide range of protocol durations. In the second part of his talk, Deepak will show his recent experimental results on F1ATPase motor, where they compared the dissipation of driving this motor using two experimentally viable protocols: angle clamp and torque clamp. The experimental results (supported by analytical findings) suggest that angle clamp driving requires less work than that of the torque clamp
>> DIEP Seminar: Pierre Haas (Max Planck Institute for the Physics of Complex Systems)
Examples of emergence in ecology and morphogenesis | 11am, 2nd of October 2025
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In this talk, Pierre will give three examples of different kinds of emergent behaviour in very different biological systems, starting with a curious example of the emergence of stability in ecological communities. He will show that the possibility of stable coexistence in ecological communities with Lotka-Volterra dynamics emerges from "irreducible" communities. Pierre will futher explain how our exhaustive analysis of all networks of competitive, mutualistic, and predator-prey interactions of N<6 species suggested that, strikingly, these irreducible ecologies form an exponentially small subset of all ecologies, as do the mathematically curious "impossible ecologies" in which stable coexistence is non-trivially impossible. He will briefly outline the rich mathematical structures hiding in this problem. Next, Mr. Haas will turn to an example of the emergence of shape from mechanical instabilities and geometry in development: Morphogenesis is often the active result of cellular deformations within a tissue, but can also be passive, resulting from forces applied at tissue boundaries by neighbouring active tissues. The ateendees will be introduced to the Drosophila hindgut primordium as a physical model for this boundary-driven morphogenesis. They will be shown how we combined experimental quantification and physical models to reveal how a mechanical bifurcation breaks the symmetry of the shape of the hindgut and how the geometry of ellipsoidal embryo robustly selects the orientation of this shape. Finally, he will give an example revealing how complex macroscopic dynamics can emerge from microscopic mechanics in morphogenesis. The biological context for this example is epithelial gap closure, a crucial tissue movement during development that requires cell rearrangements at the edge of the closing gap. Mr Haas will show how these plastic cell intercalations interact with the elasticity of the tissue by coarse-graining a minimal model of cell intercalations. Their work reveals that different macroscopic closure dynamics can emerge at the tissue scale from the details of the microscopic energy barrier to intercalation. As an application, this explains the mechanical role of the tissue fluidisation observed in the serosa closure process of the beetle Tribolium.
>> DIEP Seminar: Daniel Maria Busiello (University of Padua)
Information propagation in multiscale systems, from biochemical signaling to transduction mechanisms | 11am, 25th of September 2025
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The presence of interconnected fluctuating processes occurring across multiple temporal scales is a fundamental characteristic of neural networks, ecological communities, biochemical architectures, and many other complex systems. A key feature of these systems is that such processes can interact both directly and indirectly, with interactions across timescales often exhibiting intricate internal properties. This complexity makes understanding the relationships between their components a formidable challenge. In this talk, I will begin by exploring how the distinct timescales associated with each process influence their effective couplings. By examining the probabilistic structure of a general multiscale system, I will uncover the underlying principles that govern information propagation across different timescales. In doing so, I will clarify the interplay between mutual information and coupling structure, revealing the origin of the critical distinction between causal and functional interactions in complex stochastic systems. I will then demonstrate how this emerging information structure can be harnessed to study minimal models of biochemical signaling networks, encoding features of stochastic neural populations, and the performance of different nonlinear processing operations. Additionally, I will apply this framework to investigate how biological systems transduce information from hidden degrees of freedom through a set of accessible observables. I will show that, even within a limited energy budget, optimal transduction strategies can enhance information harvesting. Finally, I will apply these concepts to red blood cells, highlighting the connection between mechanical stress and transduction efficiency. The ideas presented in this talk provide novel insights into the processing capabilities of complex multiscale systems.
>> DIEP Seminar: Karsten Kruse (University of Geneva)
Physics of Tissue Development: How Defects Can Organize Morphogenesis | 11am, 18th of September 2025
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During development, living tissues undergo striking shape changes, sometimes even altering their topology. For example, in gastrulation a sphere of cells folds into a tube, while in the regeneration of the freshwater polyp Hydra, a spherical cell aggregate elongates, forms a head and a foot, and eventually grows tentacles. Such dramatic transformations arise from the collective dynamics of cells, which behave as active units capable of generating mechanical stress. A key feature underlying these processes is the orientational order displayed by many tissues. In this talk, I will present a physical framework that treats tissues as active polar fluids and show how a hydrodynamic description can capture their behavior. I will illustrate this approach with examples where defects in the orientational order are central for guiding development. I will then discuss how such defects can be stabilized and organized in active fluids, and finally consider the influence of fluctuations on these dynamic systems.
>> DIEP Seminar: Ruben Lier (University of Amsterdam)
A microscopically reversible kinetic theory of flocking | 11am, 11th of September 2025
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Flocking is an emergent phenomenon from whose study the field of active matter arose. The traditional approach to flocking is associated with the Vicsek model, which assumes that particles have a constant velocity magnitude. The consequence of this approach is that the microscopic description already incorporates many of the characteristics of the continuum behavior, thereby undermining the notion of emergence and obscuring the origin of activity. In this seminar, I will discuss a completely different approach to active matter where I consider a microscopically reversible model that involves reactive collisions between bird and air particles, both modeled as hard spheres, and I show that by turning on the "chemostat", such a model can give rise to a flocking transition.
>> DIEP Seminar: Christian Hamster (DIEP)
Random Evolutionary Dynamics in Predator-Prey Systems Yields Large, Clustered Ecosystems | 11am, 4th of September 2025
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We study the effect of introducing new species through evolution into communities. We use the setting of predator-prey systems. Predator-prey dynamics is classically well modeled by Lotka-Volterra (LV) equations, also when multiple predator and prey species co-exist. We use a stochastic method to introduce new species in a two-trophic LV system.
We find that introducing random evolving species leads to robust ecosystems in which large numbers of species coexist. Crucially, in these large ecosystems, an emergent clustering of species is observed, tying functional differences to phylogenetic history.
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>> DIEP Seminar: Leonardo Di Gaetano (Aix Marseille Université)
The Effect of Higher‑Order Interactions on Structure, Percolation, and Dynamical Fluctuations of Networks | 11am, 19th of June 2025
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This talk presents two recent studies on the role of higher-order interactions in complex networks. In the first part, we introduce a hidden-variable formalism for temporal higher-order networks that captures key structural properties of a broad class of generative random processes—such as hyperdegree distributions and correlations—and allows for analytical estimates of percolation thresholds. We show that neglecting group interactions leads to systematic biases in structural and temporal metrics.
In the second part, we focus on dynamics. Using large deviation theory, we investigate rare events and dynamical fluctuations under the influence of higher-order interactions. We demonstrate how group interactions modulate fluctuation spectra, in particular suppressing trajectories that deviate significantly from typical behavior. These effects emerge in both quenched and annealed settings, corresponding respectively to static network realizations and ensembles of time-varying structures. The mathematical framework developed here is not limited to higher-order systems and can be extended to study the predictability and fluctuation structure of a wide class of network dynamics.
>> DIEP Seminar: Hermes Bloomfield-Gadêlha (University of Bristol)
Patterns, Robots, and Tiny Motors: Exploring the Mystery of Self-Organization in Cilia and Flagella Beating | 11am, 12th of June 2025
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This talk presents updates from Polymaths Lab, Bristol, on how axonemal motors self-organize to drive cilia and flagella beating. We explore how 3D structures shape, and sometimes suppress, planar beating, and what this means for swimming cells. We created a simple yet precise reaction-diffusion model (Cass & Gadelha, Nature Communications 2023), inspired by classic chemistry where patterns emerge over time and space—like animal skin patterns. Here, the pattern is the flagellum’s rhythmic wave, driven by sliding molecular motors. Unlike most models, ours doesn’t need the flagellum to sense the fluid around it. Instead, internal friction alone can drive the wave, matching experimental beating patterns of bull sperm and Chlamydomonas algae. This suggests a universal mechanism for flagellar movement in watery environments, critical for aquatic organisms. A 3D multi-physics model further shows that planarity emerges from teams of molecular motors competing inside the axoneme. 3D microscopy reveals sperm create counter-rotating vortices and spin like tops to swim straight, even with asymmetric beats (Ren & Gadelha, Advanced Science2024). We also showcase new robotics, lab-on-chip, and imaging tools developed at Polymaths Lab to explore complex systems, including how body design and environmental interaction enable embodied intelligence, as in octopus-like suction circuits with self-emergent behaviours (Yue et al. Science Robotics 2025).
>> DIEP Seminar: Mirta Galesic (Santa Fe Institute)
Beyond collective intelligence: Collective adaptation | 11am, 5th of June 2025
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Throughout our human history and in our daily lives, we have been co-evolving our social networks and a variety of social learning strategies suitable for the particular challenges we believe are important. We are so good at moving between these different socio-cognitive constellations that we are often not aware of this constant collective adaptation – until it goes wrong. Sometimes our collectives seem stuck and unable to adapt to the problems they face, even though the solutions might seem obvious. Going beyond identifying static “intelligent” or “stupid” collectives, I will describe a conceptual framework for studying collective adaptation and related initial modeling and empirical work. This framework stresses the importance of path dependence, satisficing, and collective myopia for understanding the sometimes counterintuitive outcomes of collective adaptation. This is particularly important in today’s turbulent world in which collectives face diverse and rapidly changing challenges.
>> DIEP Seminar: Marta C. Couto (University of Amsterdam)
Evolution of Boundedly Rational Learning in Games | 11am, 22nd of May 2025
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People constantly make strategic decisions, which are essential in negotiation, coordination, or cooperation problems. The resulting dynamics of strategy adaptation can be studied with evolutionary game theory. A crucial quantity in such models is the strength of selection, which regulates how likely individuals are to switch to a better strategy. The larger the selection strength, the more biased the learning process is toward strategies with large payoffs. Thus, selection strength is often interpreted as a measure of rationality or learning effectiveness.
Most previous models assume a fixed selection strength for all players. As a result, the effect of heterogeneous selection strengths – and whether higher selection strength leads to a strategic advantage – remains unclear. To address this, we study settings where (i) individuals have different selection strengths and (ii) strategies and selection strengths co-evolve. One might expect evolution to favor ever-increasing values of selection strengths. Remarkably, however, higher selection strength does not always lead to better long-term outcomes – depending on the strategic interaction in place, we can observe convergence to a finite value, or even evolutionary branching. This work sheds light on how evolution may shape learning mechanisms for social behavior, suggesting that boundedly rational learning might evolve not only as a by-product of cognitive constraints but as a way to gain strategic advantages.
>> DIEP Seminar: Jaehyeok Jin (Columbia University)
Bottoms Up! Pouring Molecular Insights into Multiscale Modeling | 11am, 8th of May 2025
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With their intrinsically multiscale nature, molecules serve as particularly essential systems for studying multiscale modeling, with emergent properties spanning from the femto- to exa-scales. However, capturing the microscopic details underlying large-scale dynamics and providing predictive insights beyond current experimental capabilities present significant challenges, especially when these phenomena are tightly coupled across spatiotemporal scales. In this talk, I will present statistical mechanical design principles for next-generation coarse-grained models of molecular soft matter. Grounded in first-principles statistical physics, I will illustrate how to systematically develop bottom-up coarse-grained models using microscopic information to seamlessly bridge the scales—from quantum to molecular, molecular to mesoscopic, and then ultimately to the macroscopic. This physics-driven coarse-graining approach is poised to deliver predictive and explanatory capabilities for understanding complex multiscale processes.
>> DIEP Seminar: Doyne Farmer (Oxford University)
Economics: The historical mother of emergent phenomena | 11am, 29th of April 2025
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Historically, the first most famous reflections on emergence were about economics - from Adam Smith in 1776, and even earlier by Imam Al-Ghazali circa 1100. From a more modern perspective, what is the nature of emergence in economics? Where does it happen, how do we model it, and what insight does economics give us about emergence in complex systems more generally? In addition to some broad reflections, I will provide examples of simple models from complexity economics for iterated games, the financial crisis of 2008, and business cycles in general, and discuss a few empirically useful real-world examples.
>> DIEP Seminar: Vishwesha Guttal (Indian Institute of Science, Bangalore)
The role of finite size stochasticity in ecological systems: from animal groups to populations to evolution | 11am, 24th of April 2025
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Biological systems are fundamentally shaped by stochasticity. In my talk, I will demonstrate the key role of intrinsic noise, which are fluctuations arising from inherent probabilistic nature of biological interactions and are amplified in finite systems. First, I will demonstrate, using both theory and empirical data, how noise can shape order in small to intermediate-sized fish schools. Next, I will show how noise can inform us about the underlying ecological dynamics. Finally, I will show theoretically how the stochasticity of finite populations can exhibit counter-intuitive dynamics on both ecological and evolutionary time scales.
>> DIEP Seminar: Jan Korbel (Complexity Science Hub Vienna)
From Spins to Society: Modeling Collective Social Behavior with Statistical Physics | 11am, 17th of April 2025
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Sociophysics is an interdisciplinary field that applies methods from statistical physics to understand collective social phenomena, such as the emergence of polarization in societies. Two key mechanisms often cited as drivers of social dynamics are homophily—the tendency to form friendly ties with like-minded individuals—and social balance—the overrepresentation of triadic relationships where either all three connections are friendly or one is friendly, while the remaining two are hostile. These ideas are captured by the classic sayings:
"Birds of a feather flock together",
"The friend of my friend is my friend; the enemy of my enemy is my friend."
In this talk, I will present recent results showing how these two principles can be jointly modeled using tools from statistical physics. I begin by introducing a model inspired by the Ising model, in which homophilic interactions naturally give rise to social balance as an emergent property. I then show how this model can be extended to explore various forms of collective behavior, focusing in particular on the distribution of group sizes in social networks. I also examine how the topological features of the underlying network—such as node degree—relate to the rise of polarization. Finally, I will discuss how external influences, such as political or media campaigns, can be incorporated into the model to study their impact on opinion dynamics.
>> DIEP Seminar: Giovanni Petri (Northeastern University London)
Renormalization and Higher-Order Interactions: Bridging Structure and Dynamics in Complex Systems | 11am, 10th of April 2025
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In most biological systems, active motion is not a continuous, uninterrupted process but an intermittent one, where displacements occur in bursts. From bacteria to sheep, biological entities switch their active motion on and off. The transport properties of these systems are governed by the underlying decision-making mechanisms that control movement. Moreover, collective motion, from an initially static group, requires all members to transition to an active state. This transition propagates as activation waves, ultimately leading to coordinated group movement. In this talk, Fernando Peruani will introduce a general theoretical framework to explain (i) the emergence of optimal transport in bacteria, as well as (ii) behavioral synchronization, collective intelligence, and criticality in sheep.
>> DIEP Seminar: Bert Kappen (Radboud University)
Stochastic Optimal Control of Open Quantum Systems | 11am, 3rd of April 2025
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We address the generic problem of optimal quantum state preparation for open quantum systems. It is well known that open quantum systems can be simulated by quantum trajectories described by a stochastic Schrödinger equation. In this context, the state preparation becomes a stochastic optimal control (SOC) problem. The latter requires the solution of the Hamilton-Jacobi-Bellman equation, which is, in general, challenging to solve. A notable exception are the so-called path integral (PI) control problems, for which one can estimate the optimal control solution by direct sampling of the cost objective. In this work, we derive a class of quantum state preparation problems that are amenable to PI control techniques, and propose a corresponding algorithm, which we call Quantum Diffusion Control (QDC). Unlike conventional quantum control algorithms, QDC avoids computing gradients of the cost function to determine the optimal control. Instead, it employs adaptive importance sampling, a technique where the controls are iteratively improved based on global averages over quantum trajectories. We also demonstrate that QDC, used as an annealer in the environmental coupling strength, finds high accuracy solutions for unitary (noiseless) quantum control problems. We further discuss the implementation of this technique on quantum hardware. We illustrate the effectiveness of our approach through examples of open-loop control for single- and multi-qubit systems.
>> DIEP Seminar: Fernando Peruani (Paris Cergy University)
Statistical Mechanics of Intelligent Active Matter: Optimal Transport, Synchronization, and Criticality in Bacteria and Sheep Herds | 11am, 27th of March 2025
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In most biological systems, active motion is not a continuous, uninterrupted process but an intermittent one, where displacements occur in bursts. From bacteria to sheep, biological entities switch their active motion on and off. The transport properties of these systems are governed by the underlying decision-making mechanisms that control movement. Moreover, collective motion, from an initially static group, requires all members to transition to an active state. This transition propagates as activation waves, ultimately leading to coordinated group movement. In this talk, Fernando Peruani will introduce a general theoretical framework to explain (i) the emergence of optimal transport in bacteria, as well as (ii) behavioral synchronization, collective intelligence, and criticality in sheep.
>> DIEP Seminar: Fernando Nobrega Santos (University of Amsterdam)
Independent Scaling Exponents in the Brain | 11am, 20th of March 2025
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We apply the phenomenological renormalization group to resting-state fMRI time series of brain activity in a large population. By recursively coarse-graining the data, we compute scaling exponents for the series variance, log probability of silence, and largest covariance eigenvalue. The exponents clearly exhibit linear interdependencies, which we derive analytically in a mean-field approach. We find a significant correlation of exponent values with the gray matter volume and cognitive performance. Akin to scaling relations near critical points in thermodynamics, our findings suggest scaling interdependencies are intrinsic to brain organization and may also exist in other complex systems.
>> DIEP Seminar: Matteo Capucci (Advanced Research and Invention Agency)
An Elementary Account of the Internal Model Principle | 11am, 13th of March 2025
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During this session, Capucci will talk about recent work with Baltieri, Biehl and Virgo [1] on a categorical account of the classical 'internal model principle' from control theory and cybernetics in a broader sense. The aim is to distill the mathematical content of such an informal principle, following previous work of Wonham and Hepburn. In the talk Capucci will only use elementary mathematical notions and thus should be accessible to an audience acquainted with the basic vocabulary of sets and dynamical systems.
[1] Baltieri, Biehl, C., Virgo, "A Bayesian Interpretation of the Internal Model Principle", (preprint), 2025, URL: http://arxiv.org/abs/2503.00511
>> DIEP Seminar: Gemma De les Cover (Universitat Pompeu Fabra)
Universality in Physics, Computer Science and Beyond | 11am, 6th of March 2025
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During the session, Gemma De Ies Coves will consider notions of universality in physics, computer science and others, and will examine their similarities, for example via a framework or by casting spin models as formal languages. She will also consider their relation to notions of unreachability such as undecidability and uncomputability.
>> DIEP Seminar: Abel Jansma (University of Amsterdam)
The Mereology of Higher-Order Interactions | 11am, 27th of February 2025
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A mereology is a partial order that describes a hierarchy of scales, and it turns out that committing to a mereology fixes all higher-order interactions through the Möbius inversion theorem. Abel will first show that this procedure reproduces many well-known quantities from physics, biology, chemistry, game theory, and AI. He will then demonstrate how to use the framework to derive new quantities, focusing on decompositions in information theory and interventional causality, and present a new way to calculate renormalised couplings.
>> DIEP Seminar: Ricard Solé (ICREA-Complex Systems Lab, Santa Fe Institute)
Fundamental constraints to the logic of living systems | 11am, 18th of February 2025
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It has been argued that the historical nature of evolution makes it a highly path-dependent process. Under this view, the outcome of evolutionary dynamics could result in a diverse landscape of complex agents with different forms and functions. At the same time, there is ample evidence that convergence and constraints strongly limit the domain of the potential design principles that evolution can achieve. Are these limitations relevant in shaping the fabric of the possible? Here, we argue that fundamental constraints are associated with the logic of living matter. We illustrate this idea by considering the thermodynamic properties of living systems, the linear nature of molecular information, the cellular nature of the building blocks of life, its open-endedness, the threshold nature of computations in cognitive systems, language and the discrete nature of the architecture of ecosystems. In all these examples, we present available evidence and suggest potential avenues towards a well-defined theoretical formulation.
>> DIEP Seminar: Gabriel Coutinho (Federal University of Minas Gerais)
The combinatorics of quantum walks | 11am, 13th of February 2025
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Imagine a walker that can access a finite number of sites and, at each step, moves to a neighboring site with a certain probability. But now, the walker is quantum. What exactly does this mean? In this talk, we will explore how quantum walks serve as the quantum analogue of classical random walks and, more importantly, what the combinatorics of the underlying graph can tell us about their behavior. We will also briefly survey results from various branches of mathematics that contribute to the study of quantum walks and discuss some open problems.
>> DIEP Seminar: Lourens Waldorp (University of Amsterdam)
Mean field theory of the general-spin Ising model | 11am, 6th of February 2025
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In psychology, the Ising model is often used to model pathologies like depression, where symptoms are represented as nodes and their associations as links. However, a limitation of the traditional Ising model is its binary nature, which fails to capture more subtle variations in states. The general-spin Ising model extends this by allowing 2k + 1 spin values: −1, −(k+1)/k, …, 0, 1/k, …, 1. In this talk, we derive the mean field of the general-spin Ising model using the variational principle of Gibbs free energy. Like the standard Ising model, it exhibits spontaneous magnetization, but with a shift depending on the number of categories. Additionally, the hysteresis effect decreases as the number of spin categories increases. Monte Carlo simulations confirm our theoretical results.
>> DIEP Seminar: Alessandro Ingrosso (Radboud University)
Statistical Mechanics of Transfer Learning in the Proportional Limit | 11am, 30th of January 2025
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Transfer learning (TL) is a well-established machine learning technique to boost the generalization performance on a specific (target) task using information gained from a related (source) task, and it crucially depends on the ability of a network to learn useful features. I will present a recent work that leverages analytical progress in the proportional regime of deep learning theory (i.e. the limit where the size of the training set P and the size of the hidden layers N are taken to infinity keeping their ratio P/N finite) to develop a novel statistical mechanics formalism for TL in Bayesian neural networks. I'll show how such single-instance Franz-Parisi formalism can yield an effective theory for TL in one-hidden-layer fully-connected neural networks. Unlike the (lazy-training) infinite-width limit, where TL is ineffective, in the proportional limit TL occurs due to a renormalized source-target kernel that quantifies their relatedness and determines whether TL is beneficial for generalization.
>> DIEP Seminar: David Saad (Aston University)
Control and mitigation of spreading processes | 11am, 23rd of January 2025
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The modern world comprises interlinked networks of contacts between individuals, computing devices and social groups, where infectious diseases, information and opinions propagate through their edges in a probabilistic or deterministic manner via interactions between individual constituents. The spread of information, opinions and marketing material can be modelled and analysed in a similar manner to that of epidemic spreading among humans or animals. To contain and mitigate the spread of infectious diseases one would like to model the spread probabilistically, implement effective prevention and mitigation policies and deploy vaccines in a way that minimises the spread. This is a difficult problem and becomes even harder in the presence of infectious but asymptomatic individual states. In the world of marketing and opinion setting, winners are those who maximise the impact by deploying resource to the most influential available nodes at the right time, occasionally in competition (or collaboration) with adversarial (supportive) spreading processes. These can represent opinion formation by political parties (competitive) or diseases that increase the susceptibility to mutual infections (collaborative). Additionally, spreading processes on different networks may be interlinked, providing additional challenge in their mitigation and an incentive to share resources. I will explain the modelling of epidemic spreading processes and present the probabilistic analytical framework for impact maximisation/minimisation we have developed, addressing the questions of vaccine (budget) deployment and spreading maximisation in single and competitive/collaborative processes. I will also present the analysis of epidemic spreading processes with infectious but asymptomatic states and of interlinked spreading processes on different networks, and the effectiveness of containment and mitigation in both cases.
