Topic: topological priors

The brain navigates mazes using the entorhinal grid and hippocampal place codes. While specific grid codes can represent maze structures independent of behavioral policy, little is known about how these task structure-specific grid codes generalize to novel tasks requiring different structures and policies. Reinforcement learning algorithms typically employ policy-biased representations, yet this approach lacks biological plausibility since it requires multiple distinct representations for task completion. To address these limitations, we propose topology-aware grid coding (TAG), a novel compu