{"ID":2863274,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03286","arxiv_id":"2510.03286","title":"A Biologically Interpretable Cognitive Architecture for Online Structuring of Episodic Memories into Cognitive Maps","abstract":"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.","short_abstract":"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 plausibi...","url_abs":"https://arxiv.org/abs/2510.03286","url_pdf":"https://arxiv.org/pdf/2510.03286v1","authors":"[\"E. A. Dzhivelikian\",\"A. I. Panov\"]","published":"2025-09-29T04:07:38Z","proceeding":"q-bio.NC","tasks":"[\"q-bio.NC\",\"cs.AI\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}