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Kuleshova, E.

Publications and source records attributed to Kuleshova, E..

2 recordsLinked to original sources

Universal Geometry of Compositional Construction in Prefrontal Cortex

Compositional generation underlies the systematic and essentially unlimited construction of complex concepts from simpler parts, as is foundational to intelligent behavior, but its underlying neural mechanisms remain unclear. Here we reveal a neural implementation of hierarchical compositional construction of abstract sequences. We demonstrate that in an open-ended setting with very sparse feedback, rats innately utilize hierarchical composition to construct adaptive action sequences that would have been difficult to discover from scratch. Prefrontal neural population representations of these abstract sequences adhere to a low-dimensional format that encodes the orderly progression of elemental units comprising the sequence while converging to a sequence-general endpoint. Higher-level compositions in the hierarchy are systematically related to their lower-level constituent parts, reusing much of the representation, while providing context separation and satisfying format constraints. These neural representations are geometrically identical across animals, pointing to a convergent solution for how knowledge is hierarchically assembled via a compositional mechanism.

neuroscience↗

Cognitive Graphs of Latent Structure in Rostral Anterior Cingulate Cortex

Mental maps of environmental structure enable the flexibility that defines intelligent behavior. We describe a striking internal representation in rat prefrontal cortex that exhibits hallmarks of a workspace for constructing goal-specific, cognitive graphs for sequences of abstract states that animals must traverse through their actions. As rats uncover -- unguided -- complex sequential patterns, neural ensemble activity in rostral Anterior Cingulate Cortex develops a highly structured yet compact, scalable representation anchored in states at the start and end of self-organized sequences. Graphs are organized to reflect relational similarities across contexts, and individually, permit flexible refinement of component states. The representations crystalline organization permits instantaneous inference of the animals current state within self-generated sequences, offering insights into the algorithmic and representational principles underlying unguided parsing of open-ended problems.

neuroscience↗