# A Biologically Interpretable Cognitive Architecture for Online Structuring of Episodic Memories into Cognitive Maps

arXiv: https://arxiv.org/abs/2510.03286 · HTML: https://mlsanity.com/paper/2510.03286/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 15 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 3 of 3)

- [Successor Features for Transfer in Reinforcement Learning](https://mlsanity.com/paper/1606.05312/) (2016)
- [Learning Cognitive Maps from Transformer Representations for Efficient Planning in Partially Observed Environments](https://mlsanity.com/paper/2401.05946/) (2024)
- [Learning Successor Features with Distributed Hebbian Temporal Memory](https://mlsanity.com/paper/2310.13391/) (2023)

## Abstract (arXiv)

Cognitive maps provide a powerful framework for understanding spatial and abstract reasoning in biological and artificial agents. While recent computational models link cognitive maps to hippocampal-entorhinal mechanisms, they often rely on global optimization rules (e.g., backpropagation) that lack biological plausibility. In this work, we propose a novel cognitive architecture for structuring episodic memories into cognitive maps using local, Hebbian-like learning rules, compatible with neural substrate constraints. Our model integrates the Successor Features framework with episodic memories, enabling incremental, online learning through agent-environment interaction. We demonstrate its efficacy in a partially observable grid-world, where the architecture autonomously organizes memories into structured representations without centralized optimization. This work bridges computational neuroscience and AI, offering a biologically grounded approach to cognitive map formation in artificial adaptive agents.