ArticleScientific reports2022
Neural network based successor representations to form cognitive maps of space and language.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Accounting for sensitivity of latent learning to behavioral statistics with successor representations.PLoS computational biology · 2026Article
- A map of words: Retrieving the spatial layout of medium-scale geographical maps through distributional semantics.Neuropsychologia · 2025Article
- Empowering safer socially sensitive autonomous vehicles using human-plausible cognitive encoding.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Deep Learning-Emerged Grid Cells-Based Bio-Inspired Navigation in Robotics.Sensors (Basel, Switzerland) · 2025Review
- Probing for consciousness in machines.Frontiers in artificial intelligence · 2025Article
- Decomposing geographical judgments into spatial, temporal and linguistic components.Psychological research · 2024Article
- Neural network based formation of cognitive maps of semantic spaces and the putative emergence of abstract concepts.Scientific reports · 2023Article
- A Theory of Mental FrameworksFrontiers in psychology · 2023Article
- Classification at the accuracy limit: facing the problem of data ambiguity.Scientific reports · 2022Article
- Neural correlates of linguistic collocations during continuous speech perception.Frontiers in psychology · 2022Article
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Authors and funding
6 authors.
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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.
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