# Neural Network based Successor Representations of Space and Language

arXiv: https://arxiv.org/abs/2202.11190 · HTML: https://mlsanity.com/paper/2202.11190/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 89 references that are in the map.
- First posted on arXiv 22 Feb 2022.

## Builds on (most cited first, 8 of 8)

- [Deep Learning: A Critical Appraisal](https://mlsanity.com/paper/1801.00631/) (2018)
- [Cognitive computational neuroscience](https://mlsanity.com/paper/1807.11819/) (2018)
- [How deep is deep enough? -- Quantifying class separability in the hidden layers of deep neural networks](https://mlsanity.com/paper/1811.01753/) (2018)
- [Learning with Known Operators reduces Maximum Training Error Bounds](https://mlsanity.com/paper/1907.01992/) (2019)
- [A statistical method for analyzing and comparing spatiotemporal cortical activation patterns](https://mlsanity.com/paper/1611.07677/) (2016)
- [Analysis of structure and dynamics in three-neuron motifs](https://mlsanity.com/paper/1811.05225/) (2018)
- [Sparsity through evolutionary pruning prevents neuronal networks from overfitting](https://mlsanity.com/paper/1911.10988/) (2019)
- [Integration of Leaky-Integrate-and-Fire-Neurons in Deep Learning Architectures](https://mlsanity.com/paper/2004.13532/) (2020)

## Built on by (most cited first, 9 of 9)

- [Word class representations spontaneously emerge in a deep neural network trained on next word prediction](https://mlsanity.com/paper/2302.07588/) (2023)
- [Neural Network based Formation of Cognitive Maps of Semantic Spaces and the Emergence of Abstract Concepts](https://mlsanity.com/paper/2210.16062/) (2022)
- [Classification at the Accuracy Limit -- Facing the Problem of Data Ambiguity](https://mlsanity.com/paper/2206.01922/) (2022)
- [Conceptual Cognitive Maps Formation with Neural Successor Networks and Word Embeddings](https://mlsanity.com/paper/2307.01577/) (2023)
- [Multi-Modal Cognitive Maps based on Neural Networks trained on Successor Representations](https://mlsanity.com/paper/2401.01364/) (2023)
- [Beyond Labels: Advancing Cluster Analysis with the Entropy of Distance Distribution (EDD)](https://mlsanity.com/paper/2311.16621/) (2023)
- [Analyzing Narrative Processing in Large Language Models (LLMs): Using GPT4 to test BERT](https://mlsanity.com/paper/2405.02024/) (2024)
- [Probing for Consciousness in Machines](https://mlsanity.com/paper/2411.16262/) (2024)
- [Word-Class and Construction-Like Structure Emerges in Neural Successor Representations Trained on Natural Language](https://mlsanity.com/paper/2605.24585/) (2026)

## Abstract (arXiv)

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.