McNamee, D. C. | bioRxiv Science

bioRxiv2026

Transformations of cognitive maps for sensorimotor control

Adaptive embodied behavior involves transforming structured knowledge about the relationship between environment and action into motor signals, but how these transformations are coordinated across brain networks remain unknown. Participants learned associations between visual cues and isometric exertions that varied in force and duration, forming a two-dimensional cognitive map of a force-time space. During behavior, this force-time space was expressed in several cortical regions using grid-like coding schemes, indicating sensorimotor cognitive maps. Importantly, while mnemonic regions such as the entorhinal cortex maintained an unwarped, task-relevant representation, the primary motor cortex encoded a force-time space distorted by perceived effort during motor execution. Dynamic causal modeling showed inhibitory motor-to-mnemonic coupling that predicted the transformation of effort-weighted motor signals into sensorimotor maps. Furthermore, individual differences in learning and navigating the force-time space independently shaped mnemonic map geometry and perceived effort. These findings demonstrate that sensorimotor cognitive maps emerge from dynamic interactions between motor and mnemonic systems and are shaped by individual differences during the learning and execution of movement.

neuroscience↗

bioRxiv2025

Topological spatial coding for rapid generalization in the hippocampal formation

The brain navigates complex environments by combining entorhinal grid codes with hippocampal place codes. Although grid codes effectively represent a current environments geometry, their capacity to generalize across topologically analogous environments with different reward and state structures remains poorly understood. We introduce topology-aware grid coding (TAG), a computational theory that leverages topological invariance to generalize to new environments with the same topological structure but with different geometry. Drawing on the Euler characteristic from algebraic topology, TAG integrates complementary neural codes built on foundational grid bases: place codes serving as 0D vertices for self-localization, boundary codes acting as 1D edges to learn policy-independent grid codes through state prediction errors, and corner codes functioning as 2D faces for identifying topologically significant states. TAG grid codes remain stable under topology-preserving deformations yet discriminate among non-isomorphic structures. TAG develops policy-independent grid codes for novel structures more rapidly and robustly than existing approaches, balancing structure and policy encoding for multi-subgoal navigation without extensive planning. Finally, we show that TAG is compatible with transformer architectures, enabling its integration into scalable neural networks. Together, the TAG theory describes the essential nature of geometric objects to explain how the entorhinal-hippocampal system maps the unique topological structure of spaces.

neuroscience↗