Dynamic Self-Loops in Networks of Passive and Active Binary Elements
Paul Baconnier1, Margot H Teunisse1,2, Martin van Hecke1,2
1AMOLF, 1098 XG Amsterdam, The Netherlands.
Physical Review Letters
|November 30, 2025
Summary
Researchers developed new interaction models to suppress self-loops in dissipative systems, enabling statistical studies of memory effects in complex materials. This advances understanding of transient responses and sequential computing.
Area of Science:
- Physics
- Materials Science
- Complex Systems
Background:
- Coupled binary element models capture memory effects in dissipative materials.
- Random interactions lead to self-loops, hindering statistical analysis and dissipative dynamics.
Purpose of the Study:
- Investigate the origin of self-loops in dissipative systems.
- Develop methods to suppress self-loops for effective statistical studies.
- Unify descriptions of passive and active multistable materials.
Main Methods:
- Analysis of energy injection and limit cycles in physical systems.
- Introduction of novel interaction ensembles.
- Development of hysteron models.
Main Results:
- Self-loops originate from energy injection and limit cycles.
- New interaction ensembles effectively suppress or eliminate self-loops.
- Enabled statistical studies of memory in large dissipative systems.
Conclusions:
- Suppression of self-loops is crucial for studying memory in dissipative systems.
- The developed models offer a unified framework for multistable materials.
- Opens new avenues for understanding transient responses and sequential computing.
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