The first version of MoNETA has been realized in the context of the DARPA SyNAPSE project, and successfully tested in a virtual environment simulating the Morris Water Maze (left, February 2011). The Morris Water Maze is a task used to probe the navigation skills of a rodent. The rat is placed in a water tank and has to use visual cues in order to locate a submerged platform and swim to it. The rat is motivated to find the platform to get out of the water tank. Researchers have studied this task at great length, so we know a great deal of the brain areas a rat utilizes in completing the task. Although an apparently simple one, solving the water maze actually requires that the integrated functioning of object recognition and localization, goal selection, and navigation be simulated in several interacting brain areas. The Sensory Object Recognition and Tracking, SORT, in pink, provides sensory input to MoNETA, while the Spatial Planning and Allocentric Representation of Knowledge (SPARK, in green) implements navigation via a neurally-based bidirectional graph search for route planning to learn locations of rewards/punishments, and determine the next steps for the animat within the virtual environment. The MOtivation, Reward, and goal SELection (MoRSel, in yellow) implemets a simple drives representation that, in conjuction with the rest of the animat brain, contributes to generate "intention" in the simulated agent.
Figure Left: MoNETA is a whole-brain-system model consisting of sensory, motivation, and navigation areas interfaced with a virtual environment or robotic platform. Right: MoNETA learns to perform the Morris Water Maze task. In the first trial, MoNETA explores its environment driven by lack of comfort and curiosity drives. Once it accidentally swims on top of the submerged platform (green), MoNETA learns its position by using some visual landmark at the border of the pool. As training progresses, MoNETA is able to swim directly from its current position to the platform. MoNETA integrates sensory information into higher order representations of its emerging reality, and is able to react to novel situations not explicitly programmed within the software. It can perceive its surroundings, decide what is useful, and in certain applications even be able to formulate plans that ensure its survival. MoNETA is motivated by analogs of the same drives that underlie the behavior of rats, cats, or humans.