Complexity, Economics & Statistical Physics 

Jean-Philippe Bouchaud, Académie des Sciences (France) 

Abstract: In a radically complex world, rational solutions are impossible to determine, not even to learn. One has to turn to satisficing solutions that are generically exponentially numerous. Every agent (even rational) will choose a different one, thereby creating de facto (if not de jure) heterogeneities. Simple models of a complex world necessarily generate a multiplicity of possible emergent properties, often (but not always) separated by “phase transitions”. We will review how all these ideas may come together and be disciplined by tools from statistical physics, in particular concerning disordered, high dimensional models. The overarching idea of phase diagrams and the distinction between stiff and sloppy directions may allow one to build a qualitative, scenario-based approach to macroeconomics, trading the whimsical pursuit of numerically precise predictions with the aim of being “roughly right”. 

Date: 6th June 2024  - 15.00 (Italian time)

Learning and memory in brain networks: Insights from Statistical Physics 

Nicolas Brunel, Duke University

Abstract: Memories are thought to be stored in brain networks thanks to modifications of synaptic connectivity.  Mathematical models of synaptic plasticity (sometimes called `synaptic plasticity rules' or `learning rules') capture experimental data on plasticity with increasing accuracy, but it is still unclear how realistic synaptic plasticity rules shape network dynamics and information storage.  In this talk, I will first review approaches for inferring learning rules from neurophysiological data. I will describe in particular a new approach that infers learning rules from in vivo

electrophysiological data, using experiments that compare the statistics of responses of neurons to sets of novel and familiar stimuli.  I will then focus on how the inferred learning rules shape the dynamics of networks, leading to a diversity of attractors, depending on parameters (fixed point attractors, chaotic attractors, or transient sequential activity). Finally, I will show that learning rules inferred from data are close to maximizing information storage in a space of unsupervised learning rules.

Date: 4th May 2023  - 16.30 (Italian time)

The quantum Mpemba effect 

Pasquale Calabrese, SISSA, Trieste

Abstract: The Mpemba effect is the counterintuitive and controversial phenomenon that hot water cools faster than cold one. Here I will introduce an analogous effect recently proposed and observed in extended quantum systems in which a symmetry is explicitly broken by the initial state, but it is restored by the time evolution. To study this phenomenon we introduce a new quantity, dubbed entanglement asymmetry, which is a measure of symmetry breaking inspired by the theory of entanglement in many-body states. 

Date: 2nd March 2023  - 16.30 (Italian time)

Departure from equilibrium and signatures of irreversibility in flocking systems

Irene Giardina, Sapienza University & ISC-CNR, Rome

Abstract: Biological groups such as insect swarms and bird flocks are considered as paradigmatic examples of `active' living matter, where self-propelled interacting individuals give rise to collective patterns over large scales. Activity, i.e. the ability of individuals to convert energy into motion, is a crucial feature of these systems. Experiments and theoretical analysis show that - combined with interactions - activity enhances ordering and gives rise to a novel class of dynamic critical behaviour. Activity is primarily related to energy injection and it drives the system out of equilibrium. In this talk, using simple models of collective motion, I will discuss how to quantify the departure from equilibrium in polar active systems, and how to connect it with the emergence of spontaneous order and collective response. I will finally show that signatures of irreversibility can be directly measured from the data and manifest as asymmetries in the two-body distribution.

Date: 26 May 2022  - 16.30 (Italian time)

Stochastic resetting: how to avoid wandering off in the wrong direction

Martin Evans, University of Edinburgh 

Abstract: In this talk I will review the idea of stochastic resetting: by returning some dynamical process to its initial condition one can significantly change the behaviour of the process and indeed improve the typical time to complete some complex task. The essence of the idea is that by resetting one cuts off errant trajectories.
I will discuss the simple example of a diffusive particle whose position is reset randomly in time with a constant rate r to some fixed point (e.g. its initial position). This simple system already exhibits the main features of interest induced by resetting:
(i) the system reaches a nontrivial nonequilibrium stationary state
(ii) the mean time for the particle to reach a target is rendered finite and has a minimum, optimal, value as a function of the resetting rate r.
I will discuss generalisations such as non-Markovian resetting, resetting with memory and applications to chemical reactions and search processes.

Evans MR and Majumdar SN  Diffusion with stochastic resetting Phys.Rev.Lett. 106 160601 (2011)
Evans MR, Majumdar SN and Schehr G  Stochastic resetting and applications J. Phys. A: Math. Theor 53 193001 (2020)

Date: 24 March 2022  - 16.30 (Italian time)

Biomimetic navigation of complex natural environments

Massimo Vergassola, Laboratoire de Physique, École Normale Supérieure, CNRS, Paris

Abstract: Living systems face the challenge of navigating natural environments shaped by non-trivial physical mechanisms. Notable examples are provided by long-distance orientation using airborne olfactory cues transported by turbulent flow, the tracking of surface-bound trails of odor cues, and flight in the lowest layers of the atmosphere. Terrestrial animals, insects, and birds have evolved navigation strategies that accomplish the above tasks with an efficiency that is often surprising and yet unmatched by human technology. Indeed, robotic applications for olfactory sniffers and unmanned aerial vehicles face similar challenges for the automated location of explosives, chemical, and toxic leaks, as well as the monitoring of biodiversity, surveillance, disaster relief, cargo transport, and agriculture. The interdisciplinary interplay between biology, physics, and robotics is key to jointly advancing fundamental understanding and technology. I shall review the above natural phenomena, then discuss the physics that constrains and shapes the navigation tasks, how machine-learning methods are brought to bear on those tasks, and conclude with the relevant strategies of behavior and open issues.

Date: 27 January 2022  - 16.30 (Italian time)

Quantum bound to chaos and Fluctuation-Dissipation relation

Jorge Kurchan, Laboratoire de Physique Statistique, École Normale Supérieure

Abstract: Chaos may be defined for quantum systems on the basis of an analogue of the Lyapunov exponent.
It turns out that the value of the Lyapunov exponent can take a maximal value $=2\pi \hbar /T$, the 'bound to chaos'.
It has now become clear that this bound may be derived from  the quantum fluctuation-dissipation relation, the KMS relation, revealing it as a bona-fide thermodynamic concept.

Date: 25 November 2021  - 16.30 (Italian time)

Microscopic theory of the fluctuating hydrodynamics in nonlinear lattices 

Keiji Saito, Keio University

Abstract: The theory of fluctuating hydrodynamics has been an important tool for analyzing macroscopic behavior in nonlinear lattices. However, despite its practical success, its microscopic derivation is still incomplete. In this work, we provide the microscopic derivation of fluctuating hydrodynamics, using the coarse-graining and projection technique; the equivalence of ensembles turns out to be critical. The Green-Kubo (GK) like formula for the bare transport coefficients is presented in a numerically computable form. Our numerical simulations show that the bare transport coefficients exist for a sufficiently large but finite coarse-graining length in the infinite lattice within the framework of the GK like formula. This demonstrates that the bare transport coefficients uniquely exist for each physical system. 

Date: July 22, 2021  - 12.00 (Italian time)

Quantum Simulation & Measurement of Entanglement 

Peter Zoller, Center for Quantum Physics, University of Innsbruck, and Institute for Quantum Optics and Quantum Information, Innsbruck, Austrian Academy of Sciences

Abstract: Cold atoms and ions constitute not only a platform to build highly controlled quantum many-body systems, but also provide us with a unique toolbox to develop and implement novel measurement protocols for many-body observables. Here we discuss a 'randomized measurement toolbox', where we apply random unitaries to many-body quantum states in the quantum simulator, and statistical correlations between measured probabilities provides access to quantities characterizing entanglement. This includes (Renyi) entanglement entropies, or even a complete `learning' of the entanglement Hamiltonian and entanglement spectrum. We illustrate our theoretical concepts with experimental data showing build-up of entanglement in quench dynamics, which were taken on a programmable trapped-ion simulator realizing a long-range transverse Ising model. 

Date: May 27, 2021 - 16.30 (Italian time)

Manipulating molecules one at a time: from energy to information 

Felix Ritort, Small Biosystems Lab, Departament de Física de la Matèria Condensada, Facultat de Física, Universitat de Barcelona 

Abstract: “Take a single DNA molecule and pull from its extremities while recording the force-extension curve until it gets fully straightened.” This thought experiment, which was just a dream a few decades ago, has become standard in many research labs worldwide. Force spectroscopy techniques (such as laser optical tweezers) are a fabulous tool for manipulating and monitoring biological molecules' direct action at the individual level. By measuring forces in the piconewton range and energies roughly equivalent to 1kT, we have the experimental accuracy in resolving the thermal energy unit. Recent advances in such technologies combined with theoretical developments in nonequilibrium physics offer the exciting prospect of experimentally testing fundamental physical principles and concepts in single-molecule experiments [1].

This talk will illustrate laser optical tweezers for single-molecule manipulation and some of the main applications in physics and biology. In particular, I will also present results on a Maxwell demon's experimental realization using single DNA molecules pulled under feedback protocols as a pioneering example of the thermodynamics of data processing and information [2].

1. F. Ritort, The noisy and marvelous molecular world of biology, Inventions, 4(2) (2019) 24
2. M. Ribezzi-Crivellari and F. Ritort, Large work extraction and the Landauer limit in the Continuous Maxwell Demon, Nature Physics 15 (2019) 660–664 

Date: March 25, 2021 - 16.30 (Italian time)

Spontaneous vs. stimulated brain activity: A statistical physics approach 

Lucilla De Arcangelis, University of Campania "Luigi Vanvitelli" 

Abstract: The understanding of the fundamental relation between brain resting activity and the response to stimuli is a long-standing fascinating question. Recent experiments have shown that the spontaneous brain activity is characterized by avalanches showing absence of characteristic size, result successfully interpreted in the context of criticality. However, in order to support the idea that the brain acts close to a critical point it is crucial to evidence the existence of long-range correlations. The temporal organization of neuronal avalanches in the rat cortex in vitro is characterized by a complex organization, leading to the characteristic brain oscillations and a dynamic balance between excitation and inhibition. The question concerning the relation between spontaneous and evoked activity is addressed by means of the coarse-grained Wilson Cowan model. An approach inspired in non-equilibrium statistical physics allows to derive a fluctuation-dissipation relation, suggesting that measurements of the correlations in spontaneous fluctuations in the brain activity alone could provide a prediction for the system response to a stimulus. Theoretical predictions are in good agreement with MEG data for healthy patients performing visual tasks. 

Date: February 4, 2021 - 16.30 (Italian time)

Absolute Negative Mobility: can it take place in thermal equilibrium? 

David Mukamel, Weizmann Institute of Science

Abstract: Absolute Negative Mobility (ANM) is a phenomenon whereby current in a stationary system  is  in a direction opposite to the driving field. Naïve argument suggests that ANM cannot take place in systems in thermal equilibrium as this could lead to a violation of the second law of thermodynamics. Thus numerous previous theoretical and experimental studies of ANM have dealt with the response to a driving field in  nonequilibrium  steady states. In this talk a simple lattice model of a driven tracer is introduced and demonstrated to exhibit ANM in equilibrium, with no violation of the basic laws of thermodynamics.  The limits of validity of the naïve argument are elucidated and the entropy production which accompany the motion of the tracer is calculated. 

Date: November 19, 2020 - 16.30 (Italian time)

Rough landscapes and glass dynamics: from inference to machine learning 

Chiara Cammarota, Università di Roma "La Sapienza", King's College London

Abstract: The realm of statistical mechanics has been enlarged to describe systems, such as glass forming materials, where structural disorder plays the predominant role. Interestingly the spectrum of applications of this new physics goes much beyond the scope of condensed matter and extends to the currently booming field of data science. In this colloquium I will focus on the challenge of signal-reconstruction from noisy collections of data, omnipresent in machine learning applications and in classical inference problems. By leveraging tools and ideas from glass physics, I will show how we can describe, predict, and enhance the performances of algorithms introduced to tackle these reconstruction problems. 

Date:  October 15, 2020 - 16.30 (Italian time)

Simplicial complexes and dynamics

Ginestra Bianconi, School of Mathematical Sciences, Queen Mary  University of London 

Abstract: Networks are everywhere and they describe a large variety of complex systems such as social networks and the brain. In the last few decades, unveiling the underlying architecture of complex systems using the network approach has been key to reveal how the statistical properties of network structure affect dynamics, including most notably epidemic spreading and network robustness. Recently is has been realized that many complex systems such as brain networks and social networks include interactions among two or more nodes. These complex systems cannot be captured by networks including exclusively pairwise interactions, rather these systems should be represented by higher-order networks such as simplicial complexes.  

In this talk I will show that taking into account higher-order interactions and combining network theory with topology can greatly enhance the ability to predict the function of complex systems starting from their structure.

I will overview recent results on the interplay between network topology and dynamics focusing on percolation and on synchronization phenomena.

 A new topological approach [1] to synchronization on simplicial complexes will be presented.  Here the theory of synchronization is combined with topology (specifically Hodge theory) for formulating the higher-order Kuramoto model that uses the higher-order Laplacians and  provides the main synchronization route for topological signals.  I will show that the dynamics defined on links can be projected to a dynamics defined on nodes and triangles that undergo a synchronization transition.

 This model can be applied to study synchronization of topological signals in the  brain and in biological transport networks as it proposes a new set of topological transformations  that can reveal  collective synchronization phenomena that could go  unnoticed otherwise.

 [1] Millán AP, Torres JJ, Bianconi G. Explosive higher-order Kuramoto dynamics on simplicial complexes. Physical Review Letters. 2020 May 27;124(21):218301

Date:  July 16, 2020 - 16.30 (Italian time)

A perspective view on fully developed turbulence

Roberto Benzi, Università Roma "Tor Vergata"

Abstract: In this talk I discuss some basic physical  properties of three dimensional fully developed turbulence. In particular, I will focus on the case of homogeneous and isotropic turbulence (H.I.T). This is a relatively narrow view of turbulence when compared to the enormous number of problems where turbulence plays a significant role. In fact, there exist many different turbulent "problems". Nevertheless, in the case of H.I.T there has been a remarkable scientific effort over the last few decades which, probably for the first time, provided a well defined theoretical framework  with a significant agreement against laboratory and/or numerical data. The aim of this talk is to review this effort and to illustrate our present knowledge on the problem. 

Date:  May 22, 2020 - 16.30  (Italian time)