Topic: attractor landscape

The ability to reason compositionally is a hallmark of intelligent behavior. This algebraic capacity to assemble new concepts from novel combinations of existing components lies at the heart of many human intellectual endeavors: language, math, science, philosophy, and much more. Further, recent discoveries in animal cognition indicate that compositional reasoning may not be a uniquely human trait --examples include numeracy in non-human primates, spatial navigation using cognitive maps in rodents, and even the use of basic syntax in songbirds (Tervo, Tenenbaum, & Gershman 2016) suggesting

Perceptual decision-making is crucial for survival, requiring the integration of dynamic sensory evidence from the environment with internal state variables (e.g. context, priors, value) to choose an appropriate action plan. Neural correlates of perceptual decisions are more distributed than once thought, having been found within the cortex, striatum, and superior colliculus (SC). Recent work has suggested a potentially novel node in this network - the midbrain reticular nucleus (MRN) - which was one of the few regions that encoded visual and action selection signals in a brain-wide survey of

Predicting the future outcomes of actions forms the basis for reinforcement learning to shape goal-directed behaviours. Recent work showed that learning based on predicting future sensory experience using the current state and action of an agent leads to representations that resemble those in the brain, for instance place and grid cells of the medial temporal lobe. Here we ask if combining ongoing predictive learning of sensory events and of notional value of actions leading to rewards forms representations that enable more efficient goal-directed learning than reinforcement learning alone. Si