Predicate learning in neural systems: Discovering latent generative
  structures
  Humans learn complex latent structures from their environments (e.g., natural
language, mathematics, music, social hierarchies). In cognitive science and
cognitive neuroscience, models that infer higher-order structures from sensory
or first-order representations have been proposed to account for the complexity
and flexibility of human behavior. But how do the structures that these models
invoke arise in neural systems in the first place? To answer this question, we
explain how a system can learn latent representational structures (i.e.,
predicates) from experience with wholly unstructured data. During the process
of predicate learning, an artificial neural network exploits the naturally
occurring dynamic properties of distributed computing across neuronal
assemblies in order to learn predicates, but also to combine them
compositionally, two computational aspects which appear to be necessary for
human behavior as per formal theories in multiple domains. We describe how
predicates can be combined generatively using neural oscillations to achieve
human-like extrapolation and compositionality in an artificial neural network.
The ability to learn predicates from experience, to represent structures
compositionally, and to extrapolate to unseen data offers an inroads to
understanding and modeling the most complex human behaviors.
