Exploring Criticality as a Generic Adaptive Mechanism - Aguilera, Miguel; Bedia, Manuel G. (Frontiers in Neurorobotics, 2018)
Many biological and cognitive systems do not operate deep within one or other regime of activity. Instead, they are poised near points of critical behaviour. Certain authors link some of the properties of criticality with the ability of living systems to generate autonomous or intrinsically generated behaviour. However, these claims remain highly speculative. In this paper, we aim to explore the connection between criticality and autonomous behaviour through conceptual models showing how embodied agents may adapt themselves towards critical points. We propose to exploit maximum entropy models and their formal descriptions of indicators of criticality to present a learning model for driving generic agents towards critical points. Specifically, we derive such a learning model in an embodied Boltzmann machine by implementing a gradient ascent rule maximizing the heat capacity of the controller in order to make the network maximally sensitive to external perturbations. We test and corroborate the model implementing an embodied agent in the Mountain Car benchmark test, controlled by a Boltzmann machine that adjusts its weights according to the model. We find that the neural controller reaches an apparent point of criticality, which coincides with a transition point of the behaviour of the agent between two regimes of behaviour, maximizing the synergistic information between its sensors and the combination of hidden and motor neurons. Finally, we discuss the potential of our learning model to answer questions about the connection between criticality and the capabilities of living systems to autonomously generate intrinsic constraints on their behaviour. We suggest that these 'critical agents' are able to acquire flexible behavioural patterns useful for developing successful strategies in different contexts.