# Space is a latent sequence: Structured sequence learning as a unified theory of representation in the hippocampus

arXiv: https://arxiv.org/abs/2212.01508 · HTML: https://mlsanity.com/paper/2212.01508/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2022.

## Built on by (most cited first, 6 of 6)

- [Graph schemas as abstractions for transfer learning, inference, and planning](https://mlsanity.com/paper/2302.07350/) (2023)
- [Learning non-Markovian Decision-Making from State-only Sequences](https://mlsanity.com/paper/2306.15156/) (2023)
- [Exploring and Learning Structure: Active Inference Approach in Navigational Agents](https://mlsanity.com/paper/2408.05982/) (2024)
- [Time Makes Space: Emergence of Place Fields in Networks Encoding Temporally Continuous Sensory Experiences](https://mlsanity.com/paper/2408.05798/) (2024)
- [Learning Dynamic Cognitive Map with Autonomous Navigation](https://mlsanity.com/paper/2411.08447/) (2024)
- [On learning spatial sequences with the movement of attention](https://mlsanity.com/paper/2311.06856/) (2023)

## Abstract (arXiv)

Fascinating and puzzling phenomena, such as landmark vector cells, splitter cells, and event-specific representations to name a few, are regularly discovered in the hippocampus. Without a unifying principle that can explain these divergent observations, each experiment seemingly discovers a new anomaly or coding type. Here, we provide a unifying principle that the mental representation of space is an emergent property of latent higher-order sequence learning. Treating space as a sequence resolves myriad phenomena, and suggests that the place-field mapping methodology where sequential neuron responses are interpreted in spatial and Euclidean terms might itself be a source of anomalies. Our model, called Clone-structured Causal Graph (CSCG), uses a specific higher-order graph scaffolding to learn latent representations by mapping sensory inputs to unique contexts. Learning to compress sequential and episodic experiences using CSCGs result in the emergence of cognitive maps - mental representations of spatial and conceptual relationships in an environment that are suited for planning, introspection, consolidation, and abstraction. We demonstrate that over a dozen different hippocampal phenomena, ranging from those reported in classic experiments to the most recent ones, are succinctly and mechanistically explained by our model.