2021/07/01 by Marcus Lewis, Lewis, Marcus
Biochemistry, Genetics and Molecular Biology · Computer Science · Neuroscience · Psychology · #Artificial Intelligence (cs.AI) #Artificial intelligence #Cognition #Computer science #Entorhinal cortex #FOS: Biological sciences #FOS: Computer and information sciences #Geography #Graph #Hippocampal formation #Memory and Neural Mechanisms #Neurons and Cognition (q-bio.NC) #Neuroscience #Neuroscience and Neuropharmacology Research #Pattern recognition (psychology) #Psychology #Spatial analysis #Spatial cognition #Spatial learning #Spatial relation #Theoretical computer science #Zebrafish Biomedical Research Applications #cs.AI #q-bio.NC
paper · pdf · doi:10.48550/arxiv.2107.00567
9 pages, 4 figures, writeup of poster for 30th Annual Computational Neuroscience Meeting (CNS 2021)
arxiv created 2021/07/01 · openalex publication_date 2021/07/01 · arxiv updated 2021/07/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.