A single recurrent network trained on masked sensory prediction and motion develops co-emergent grid and place cells that qualitatively match multiple experimental observations without any spatial supervision.
Stachenfeld, Matthew M
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AMLE graph value extensions meet a local action-gap certificate guaranteeing goal-reaching greedy rollouts under argmin-Q planning and achieve 0.97 success on AntMaze-derived graphs versus 0.58 for harmonic extension.
A model combining distributional successor features with successor representations supports RL in noisy partially observable environments.
Squirrel behaviors supply a comparative template for a hierarchical control model that integrates latent dynamics, episodic memory, observer beliefs, and delayed verification in agentic AI.
citing papers explorer
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A simple model of co-emergence of grid and place fields
A single recurrent network trained on masked sensory prediction and motion develops co-emergent grid and place cells that qualitatively match multiple experimental observations without any spatial supervision.
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Planner-Admissible Graph-PDE Value Extensions for Sparse Goal-Conditioned Planning
AMLE graph value extensions meet a local action-gap certificate guaranteeing goal-reaching greedy rollouts under argmin-Q planning and achieve 0.97 success on AntMaze-derived graphs versus 0.58 for harmonic extension.
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A neurally plausible model learns successor representations in partially observable environments
A model combining distributional successor features with successor representations supports RL in noisy partially observable environments.
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Coupled Control, Structured Memory, and Verifiable Action in Agentic AI (SCRAT -- Stochastic Control with Retrieval and Auditable Trajectories): A Comparative Perspective from Squirrel Locomotion and Scatter-Hoarding
Squirrel behaviors supply a comparative template for a hierarchical control model that integrates latent dynamics, episodic memory, observer beliefs, and delayed verification in agentic AI.