Neural network based successor representations to form cognitive maps of space and language

Language
en
Document Type
Article
Issue Date
2023-07-04
Issue Year
2022
Authors
Stoewer, Paul
Schlieker, Christian
Schilling, Achim
Metzner, Claus
Maier, Andreas
Krauss, Patrick
Editor
Publisher
Springer Nature
Abstract

How does the mind organize thoughts? The hippocampal-entorhinal complex is thought to support domain-general representation and processing of structural knowledge of arbitrary state, feature and concept spaces. In particular, it enables the formation of cognitive maps, and navigation on these maps, thereby broadly contributing to cognition. It has been proposed that the concept of multi-scale successor representations provides an explanation of the underlying computations performed by place and grid cells. Here, we present a neural network based approach to learn such representations, and its application to different scenarios: a spatial exploration task based on supervised learning, a spatial navigation task based on reinforcement learning, and a non-spatial task where linguistic constructions have to be inferred by observing sample sentences. In all scenarios, the neural network correctly learns and approximates the underlying structure by building successor representations. Furthermore, the resulting neural firing patterns are strikingly similar to experimentally observed place and grid cell firing patterns. We conclude that cognitive maps and neural network-based successor representations of structured knowledge provide a promising way to overcome some of the short comings of deep learning towards artificial general intelligence.

Journal Title
Scientific Reports
Volume
12
Citation
Scientific Reports 12 (2022): 11233. <https://www.nature.com/articles/s41598-022-14916-1>
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