Hippocampal Spatial Mapping As Fast Graph Learning
- URL: http://arxiv.org/abs/2107.00567v1
- Date: Thu, 1 Jul 2021 16:05:42 GMT
- Title: Hippocampal Spatial Mapping As Fast Graph Learning
- Authors: Marcus Lewis
- Abstract summary: 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.
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.
This core idea of associating arbitrary information with nodes and edges is not inherently spatial, so this proposed fast-
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: 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.
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