Motivational Cognitive Maps for Self-Regulated Autonomous Navigation

The mammalian hippocampal formation plays a critical role in efficient and flexible navigation. Hippocampal place cells exhibit spatial tuning, characterized by increased firing rates when an animal occupies specific locations in its environment. However, the mechanisms underlying the encoding of spatial information by hippocampal place cells remain not fully understood. Evidence suggests that spatial preferences are shaped by multimodal sensory inputs. Yet, existing hippocampal models typically rely on a single sensory modality, overlooking the role of interoceptive information in the formation of cognitive maps. In this paper, we introduce the Motivational Hippocampal Autoencoder (MoHA), a biologically inspired model that integrates interoceptive (motivational) and exteroceptive (visual) information to generate motivationally modulated cognitive maps. MoHA captures key hippocampal firing properties across different motivational states and, when embedded in a reinforcement learning agent, generates adaptive internal representations that drive goal-directed foraging behavior. Grounded in the principle of biological autonomy, MoHA enables the agent to dynamically adjust its navigation strategies based on internal drives, ensuring that behavior remains flexible and context-dependent. Our results show the benefits of integrating motivational cognitive maps into artificial agents with a varying set of goals, laying the foundation for self-regulated multi-objective reinforcement learning.