Hippocampal Spatial Mapping As Fast Graph Learning
This work addresses the problem of inefficient spatial learning in neuroscience and AI, offering a novel approach that could expand to various spatial and non-spatial tasks.
The paper tackles the inefficiency of hippocampal spatial mapping by proposing a fast graph learning algorithm that represents environments as graphs of sparse parts, achieving more efficient learning than traditional lookup-table models.
The hippocampal formation is thought to learn spatial maps of environments, and in many models this learning process consists of forming a sensory association for each location in the environment. This is inefficient, akin to learning a large lookup table for each environment. Spatial maps can be learned much more efficiently if the maps instead consist of arrangements of sparse environment parts. In this work, I approach spatial mapping as a problem of learning graphs of environment parts. Each node in the learned graph, represented by hippocampal engram cells, is associated with feature information in lateral entorhinal cortex (LEC) and location information in medial entorhinal cortex (MEC) using empirically observed neuron types. Each edge in the graph represents the relation between two parts, and it is associated with coarse displacement information. This core idea of associating arbitrary information with nodes and edges is not inherently spatial, so this proposed fast-relation-graph-learning algorithm can expand to incorporate many spatial and non-spatial tasks.