# How to build a cognitive map: insights from models of the hippocampal formation

arXiv: https://arxiv.org/abs/2202.01682 · HTML: https://mlsanity.com/paper/2202.01682/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 175 references that are in the map.
- First posted on arXiv 3 Feb 2022.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Decision Transformer: Reinforcement Learning via Sequence Modeling](https://mlsanity.com/paper/2106.01345/) (2021)
- [RL2: Fast Reinforcement Learning via Slow Reinforcement Learning](https://mlsanity.com/paper/1611.02779/) (2016)
- [Reinforcement Learning and Control as Probabilistic Inference: Tutorial and Review](https://mlsanity.com/paper/1805.00909/) (2018)
- [Towards a Definition of Disentangled Representations](https://mlsanity.com/paper/1812.02230/) (2018)
- [DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learning](https://mlsanity.com/paper/2006.08381/) (2020)
- [Neural Episodic Control](https://mlsanity.com/paper/1703.01988/) (2017)
- [Emergence of grid-like representations by training recurrent neural networks to perform spatial localization](https://mlsanity.com/paper/1803.07770/) (2018)
- [Hippocampal Spatial Mapping As Fast Graph Learning](https://mlsanity.com/paper/2107.00567/) (2021)
- [Prediction and Generalisation over Directed Actions by Grid Cells](https://mlsanity.com/paper/2006.03355/) (2020)
- [Analogy as Nonparametric Bayesian Inference over Relational Systems](https://mlsanity.com/paper/2006.04156/) (2020)

## Built on by (most cited first, 5 of 5)

- [ALPINE: Unveiling the Planning Capability of Autoregressive Learning in Language Models](https://mlsanity.com/paper/2405.09220/) (2024)
- [On the link between conscious function and general intelligence in humans and machines](https://mlsanity.com/paper/2204.05133/) (2022)
- [Successor Representation Active Inference](https://mlsanity.com/paper/2207.09897/) (2022)
- [SpaceMind++: Toward Allocentric Cognitive Maps for Spatially Grounded Video MLLMs](https://mlsanity.com/paper/2605.09449/) (2026)
- [Differentiable Clone-Structured Causal Graphs for End-to-End Cognitive Map Learning from Image Sequences](https://mlsanity.com/paper/2607.12382/) (2026)

## Abstract (arXiv)

Learning and interpreting the structure of the environment is an innate feature of biological systems, and is integral to guiding flexible behaviours for evolutionary viability. The concept of a cognitive map has emerged as one of the leading metaphors for these capacities, and unravelling the learning and neural representation of such a map has become a central focus of neuroscience. While experimentalists are providing a detailed picture of the neural substrate of cognitive maps in hippocampus and beyond, theorists have been busy building models to bridge the divide between neurons, computation, and behaviour. These models can account for a variety of known representations and neural phenomena, but often provide a differing understanding of not only the underlying principles of cognitive maps, but also the respective roles of hippocampus and cortex. In this Perspective, we bring many of these models into a common language, distil their underlying principles of constructing cognitive maps, provide novel (re)interpretations for neural phenomena, suggest how the principles can be extended to account for prefrontal cortex representations and, finally, speculate on the role of cognitive maps in higher cognitive capacities.