REPRESENTATION THEORY MEETS ANATOMY: FACTOR LEARNING IN THE HIPPOCAMPAL FORMATION

摘要

In this paper we argue that computational issues like complexity, memory requirements and training time impose strong constraints on learning in any goal-oriented system. Along these constraints we derive a particular architecture that learns representations for optimizing plans e.g., trajectory planning. To comply with biological constraints as well, the resulting encoding mechanism is translated into a connectionist network. We argue that the goal-oriented framework implies distinct representations of place and direction in the hippocampal formation responsible for spatial navigation in mammals.