Synchronisation is a central phenomenon in nature, observed in both biological and physical systems. This analysis explores the complex relationship between synchronisation and entropy from different perspectives, including their role in the emergence of collective behaviour and structures.
1. Introduction
Synchronisation is a fundamental phenomenon in which coupled oscillators adjust their rhythms to each other. Pikovsky et al. (2003) provide a comprehensive overview of the theory of synchronisation and its applications in various systems [1].
2. Synchronisation and entropy
The relationship between synchronisation and entropy is multi-layered.
- 2.1 Local entropy reduction
In many biological systems, synchronisation can lead to a local reduction in entropy by creating order (Riedl et al., 2014) [2]. - 2.2 Total entropy increase
At the same time, synchronisation can increase the overall entropy of a system, especially in a larger environmental context (Shiogai et al., 2010) [3]. - 2.3 Definitions of entropy
The respective definition and the system under consideration significantly influence the relationship between synchronisation and entropy, for example between thermodynamic entropy and information entropy.
3. Emergence based on synchronisation
Synchronisation can serve as a mechanism for the emergence of collective behaviour and structural organisation in complex systems. Strogatz (2003) describes how spontaneous synchronisation leads to increasingly complex emergent phenomena, from fireflies to neuronal networks [4].
4. Neuroscientific perspectives
In neuroscience, the synchronisation of neuronal activity is seen as key to the development of consciousness and cognitive function. Varela et al (2001) found that temporal binding through synchronisation plays a decisive role in the integration of information in the brain [5].
5. Quantum level and synchronisation
The study of synchronisation phenomena also extends to the quantum realm. Manzano et al. (2013) show that quantum synchronisation can lead to the generation of correlations between quantum systems, potentially contributing to macroscopic quantum effects [6].
6. Biological connections
In biological studies, as explained by Cavagna et al (2018), synchronisation is crucial for collective behaviour, especially in animal communities such as bird flocks, where it leads to cognitive and interactive properties [7].
7. Information theory perspective
Synchronisation can also be seen as a process of information transfer and integration. Lizier et al. (2018) developed methods to quantify information flows in synchronised systems, which contributes to the emergence of complexity [8].
In summary, this brief analysis shows that various research studies indicate that synchronisation can be understood both as a process of entropy reduction and as a process of promoting emergent phenomena in complex systems. By coordinating individual components, synchronisation enables the emergence of ordered structures and collective behaviour.
Referenzen
[1] Pikovsky, A., Rosenblum, M., & Kurths, J. (2003). Synchronization: A universal concept in nonlinear sciences. Cambridge University Press.
[2] Riedl, M., Müller, A., & Wessel, N. (2013). Practical considerations of permutation entropy: A tutorial review. The European Physical Journal Special Topics, 222(2), 249–262.
[3] Shiogai, Y., Stefanovska, A., & McClintock, P. V. E. (2010). Nonlinear dynamics of cardiovascular ageing. Physics Reports, 488(2–3), 51–110.
[4] Strogatz, S. H. (2003). Sync: The emerging science of spontaneous order. Penguin UK.
[5] Varela, F., Lachaux, J. P., Rodriguez, E., & Martinerie, J. (2001). The brainweb: phase synchronization and large-scale integration. Nature Reviews Neuroscience, 2(4), 229-239.
[6] Manzano, G., Galve, F., Giorgi, G. L., Hernández-García, E., & Zambrini, R. (2013). Synchronization, quantum correlations and entanglement in oscillator networks. Scientific Reports, 3(1), 1439.
[7] Cavagna, A., Conti, D., Creato, C., Del Castello, L., Giardina, I., Grigera, T. S., Melillo, S., Parisi, L., & Viale, M. (2016). Dynamic scaling in natural swarms.
[8] Lizier, J. T., Prokopenko, M., & Zomaya, A. Y. (2014). A Framework for the Local Information Dynamics of Distributed Computation in Complex Systems. In M. Prokopenko (Ed.), Guided Self-Organization: Inception (Vol. 9, pp. 115–158). Springer Berlin Heidelberg.