Physical processes rely on the transmission of energy and information across scales. In the last century, theoretical tools have been developed in the field of statistical physics to infer macroscopic properties starting from a microscopic description of the system. However, less attention has been devoted to the remodelling of microscopic degrees of freedom by macroscopic feedback. In recent years, ideas from non-equilibrium physics have been applied to characterise biological and artificial intelligence systems. These systems share in common their structure in discrete scales of organisation that perform specialised functions. To correctly regulate these functions, the accurate transmission of information across scales is crucial. In this thesis we study the role of macroscopic feedback in the remodelling of microscopic degrees of freedom in two paradigmatic examples, one taken from the field of biology, the self-organisation of specialisation and plasticity in a social wasp, and one from artificial intelligence, the remodelling of deep neural networks in a stochastic many-particle system. In the first part of this thesis we study how the primitively social wasp Polistes canadensis simultaneously achieves robust specialization and rapid plasticity. Combining a unique experimental strategy correlating time-resolved measurements across vastly different scales with a theoretical approach, we characterise the re-establishment of the social steady state after queen removal. We show that Polistes integrates antagonistic processes on multiple scales to distinguish between extrinsic and intrinsic perturbations and thereby achieve both robust specialisation and rapid plasticity. Furthermore, we show that the long-term stability of the social structure relies on the regulation of transcriptional noise by dynamic DNA methylation.
In the second part of this thesis, we ask whether emergent collective interactions can be used to remodel deep neural networks. To this end, we study a paradigmatic stochastic manyparticle model where the dynamics are defined by the reaction rates of single particles, given by the output of distinct deep neural networks. The neural networks are in turn dynamically remodelled using deep reinforcement learning depending on the previous history of the system. In particular, we implement this model as a one dimensional stochastic lattice gas. Our results show the formation of two groups of particles that move in opposite directions, diffusively at early times and ballistically over longer time-scales, with the transition between these regimes corresponding to the time-scale of left/right symmetry breaking at the level of individual particles. Over a hierarchy of characteristic time-scales these particles develop emergent, increasingly complex interactions characterised by short-range repulsion and long-range attraction. As a result, the system asymptotically converges to a regime characterised by the presence of anti-ferromagnetic particle clusters. To conclude, we characterise the impact of memory effects and demographic disorder on the dynamics. Together, our results shed light on how non-equilibrium systems can employ macroscopic feedback to regulate the propagation of fluctuations across scales.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:80344 |
| Date | 08 August 2022 |
| Creators | Alsina Lopez, Adolfo |
| Contributors | Friedrich, Benjamin, Greulich, Philip, Jülicher, Frank, Technische Universität Dresden, Max Planck Institute for the Physics of Complex Systems |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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