During the last decade, different experiments using inverse statistical mechanics have explored general properties of the neural and behavioural aspects of different biological organisms. Some of the results of these experiments show that many biological and cognitive systems do not operate deep into one or other regime of activity. Instead, they exploit critical surfaces poised at transitions in their parameter space. The pervasiveness of criticality in natural systems suggests that there may be general principles inducing this behaviour.
This research line aims to deepen in the understanding of critical phenomena and other statistical properties of embodied organisms, using statistical models and information theoretical tools. As well, we work in exploiting this knowledge for the design of bio-inspired agents exploiting the statistical mechanics of real-life organisms for displaying adaptive behaviour based on general mechanisms instead of ad hoc designs.
Aguilera M & Bedia MG (2017) Learning Criticality in an Embodied Boltzmann Machine. arXiv: 1702.00614.
Aguilera M & Bedia MG (2017). Criticality as It Could Be: organizational invariance as self-organized criticality in embodied agents. arXiv:1704.05255