This work addresses the autonomous organization of biological systems. It does so by considering the boundaries of biological systems, from individual cells to Home sapiens, in terms of the presence of Markov blankets under the active inference scheme—a corollary of the free energy principle. A Markov blanket defines the boundaries of a system in a statistical sense. Here we consider how a collective of Markov blankets can self-assemble into a global system that itself has a Markov blanket; thereby providing an illustration of how autonomous systems can be understood as having layers of nested and self-sustaining boundaries. This allows us to show that: (i) any living system is a Markov blanketed system and (ii) the boundaries of such systems need not be co-extensive with the biophysical boundaries of a living organism. In other words, autonomous systems are hierarchically composed of Markov blankets of Markov blankets—all the way down to individual cells, all the way up to you and me, and all the way out to include elements of the local environment.

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This paper considers the emergence of a generalised synchrony in ensembles of coupled self-organising systems, such as neurons. We start from the premise that any self-organising system complies with the free energy principle, in virtue of placing an upper bound on its entropy. Crucially, the free energy principle allows one to interpret biological systems as inferring the state of their environment or external milieu. An emergent property of this inference is synchronisation among an ensemble of systems that infer each other. Here, we investigate the implications of neuronal dynamics by simulating neuronal networks, where each neuron minimises its free energy. We cast the ensuing ensemble dynamics in terms of inference and show that cardinal behaviours of neuronal networks – both in vivo and in vitro – can be explained by this framework. In particular, we test the hypotheses that (i) generalised synchrony is an emergent property of free energy minimisation; thereby explaining synchronisation in the resting brain: (ii) desynchronisation is induced by exogenous input; thereby explaining event-related desynchronisation and (iii) structure learning emerges in response to causal structure in exogenous input; thereby explaining functional segregation in real neuronal systems.