MoNETA brain submodules: SPARK and MoRSel
The Spatial Planning and Allocentric Representation of Knowledge (SPARK, ingreen) 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. SPARK and MoRSEL allow MoNETA to self-localize in a novel environment, generate drives to explore its environment or reach the submerged platform, and allow to progressively learn to swim directly to the goal (see figure below).
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.