Title: Learning Criticality in an Embodied Boltzmann Machine,
Abstract: 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. However, there is a lack of conceptual models explaining how
embodied agents propel themselves towards these critical points. In this paper,
we present a learning model driving an embodied Boltzmann Machine towards
critical behaviour by maximizing the heat capacity of the network. We test and
corroborate the model implementing an embodied agent in the mountain car
benchmark, controlled by a Boltzmann Machine that adjust its weights according
to the model. We find that the neural controller reaches a 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 hidden and motor neurons. Finally, we discuss the
potential of our learning model to study the contribution of criticality to the
behaviour of embodied living systems in scenarios not necessarily constrained
by biological restrictions of the examples of criticality we find in nature.