{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/one-shot-learning-of-paired-associations-by-a","title":"One-shot learning of paired association navigation with biologically plausible schemas","arxiv_id":"2106.03580","date":"2021-06-07","proceeding":null,"authors":["M Ganesh Kumar","Cheston Tan","Camilo Libedinsky","Shih-Cheng Yen","Andrew Yong-Yi Tan"],"abstract":"Schemas are knowledge structures that can enable rapid learning. Rodent one-shot learning in a multiple paired association navigation task has been postulated to be schema-dependent. We still only poorly understand how schemas, conceptualized at Marr's computational level, are neurally implemented. Moreover, a biologically plausible computational model of the rodent learning has not been demonstrated. Accordingly, we here compose an agent from schemas with biologically plausible neural implementations. The agent gradually learns a metric representation of its environment using a path integration temporal difference error, allowing it to localize in any environment. Additionally, the agent contains an associative memory that can stably form numerous one-shot associations between sensory cues and goal coordinates, implemented with a feedforward layer or a reservoir of recurrently connected neurons whose plastic output weights are governed by a 4-factor reward-modulated Exploratory Hebbian (EH) rule. A third network performs vector subtraction between the agent's current and goal location to decide the direction of movement. We further show that schemas supplemented by an actor-critic allows the agent to succeed even if an obstacle prevents direct heading, and that temporal-difference learning of a working memory gating mechanism enables one-shot learning despite distractors. Our agent recapitulates learning behavior observed in experiments and provides testable predictions that can be probed in future experiments.","url_abs":"https://arxiv.org/abs/2106.03580v4","url_pdf":"https://arxiv.org/pdf/2106.03580v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"one-shot-learning-of-paired-associations-by-a","repo_url":"https://github.com/mgkumar138/schema4one","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"one-shot-learning-of-paired-associations-by-a","repo_url":"https://github.com/mgkumar138/Oneshot_Reservoir","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"one-shot-learning","task_name":"One-Shot Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}