A Biologically Interpretable Cognitive Architecture for Online Structuring of Episodic Memories into Cognitive Maps
Fuente:
arXiv
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909823588630528 |
|---|---|
| author | Dzhivelikian, E. A. Panov, A. I. |
| author_facet | Dzhivelikian, E. A. Panov, A. I. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03286 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Biologically Interpretable Cognitive Architecture for Online Structuring of Episodic Memories into Cognitive Maps Dzhivelikian, E. A. Panov, A. I. Neurons and Cognition Artificial Intelligence 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. |
| title | A Biologically Interpretable Cognitive Architecture for Online Structuring of Episodic Memories into Cognitive Maps |
| topic | Neurons and Cognition Artificial Intelligence |
| url | https://arxiv.org/abs/2510.03286 |