{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T04:25:01Z","timestamp":1777523101081,"version":"3.51.4"},"reference-count":15,"publisher":"SAGE Publications","issue":"5-6","license":[{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T00:00:00Z","timestamp":1755734400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["R01NS135850"],"award-info":[{"award-number":["R01NS135850"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Adaptive Behavior"],"published-print":{"date-parts":[[2025,12]]},"abstract":"<jats:p>CA1 place cells in the hippocampus have been shown to exhibit directional tuning properties, forming vector fields pointing towards locations in the environment known as ConSinks (Ormond &amp; O\u2019Keefe, 2022). We present a model, inspired by these findings, for learning goal-oriented navigation tasks. Our model employs a population of place cells that develop directional preferences, and are updated via a novel reward-modulated learning rule that refines directional turning of individual cells based on experience. Agents using this model navigated to goals significantly faster and more reliably than state-of-the-art Reinforcement Learning algorithms such as Deep Q-Networks (DQN) and Proximal Policy Optimization (PPO). We also demonstrate adaptation to new goals in a manner consistent with experimental findings, where the mean ConSink location shifts towards the new goal after it is introduced. Further experiments show that the model performs well with both goal-directed and random initialization of directional sensitivity, and that place cell density enhances learning efficiency. These results suggest a functional role for directional place cells in complex and obstacle filled environments.<\/jats:p>","DOI":"10.1177\/10597123251364749","type":"journal-article","created":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T06:58:43Z","timestamp":1755845923000},"page":"279-290","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":1,"title":["Adaptive, Goal-Oriented Navigation Using a Model of Directionally-Polarized Place Cells"],"prefix":"10.1177","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-7914-4220","authenticated-orcid":false,"given":"Harrison","family":"Espino","sequence":"first","affiliation":[{"name":"University of California Irvine"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0739-2468","authenticated-orcid":false,"given":"Jeffrey L.","family":"Krichmar","sequence":"additional","affiliation":[{"name":"University of California Irvine"},{"name":"University of California Irvine"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,8,21]]},"reference":[{"key":"e_1_3_3_2_1","unstructured":"Brockman G. Cheung V. Pettersson L. Schneider J. Schulman J. Tang J. Zaremba W. (2016). Openai gym. CoRR abs\/1606.01540."},{"key":"e_1_3_3_3_1","first-page":"2829","volume-title":"Proceedings of the 33rd international conference on machine learning, volume 48 of proceedings of machine learning research","author":"Gu S.","year":"2016","unstructured":"Gu S., Lillicrap T., Sutskever I., Levine S. (2016). Continuous deep q-learning with model-based acceleration. In Balcan M. F., Weinberger K. Q. (Eds.), Proceedings of the 33rd international conference on machine learning, volume 48 of proceedings of machine learning research (pp. 2829\u20132838). PMLR."},{"key":"e_1_3_3_4_1","first-page":"27","volume-title":"From animals to animats 17. SAB 2024. Lecture notes in computer science","author":"Harrison E.","unstructured":"Harrison E., Krichmar J. L. Vector-based navigation inspired by directional place cells. In Brock O., Krichmar J. (Eds.), From animals to animats 17. SAB 2024. Lecture notes in computer science (pp. 27\u201338). Springer. https:\/\/doi.org\/10.1007\/978-3-031-71533-4_3"},{"key":"e_1_3_3_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-019-10139-7"},{"key":"e_1_3_3_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0896-6273(02)01096-6"},{"key":"e_1_3_3_7_1","doi-asserted-by":"publisher","DOI":"10.3390\/electronics8101128"},{"key":"e_1_3_3_8_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-018-0232-z"},{"key":"e_1_3_3_9_1","unstructured":"Mnih V. Kavukcuoglu K. Silver D. Graves A. Antonoglou I. Wierstra D. Riedmiller M. A. (2013). Playing atari with deep reinforcement learning. CoRR 5602. abs\/1312."},{"key":"e_1_3_3_10_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41586-022-04913-9"},{"key":"e_1_3_3_11_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.aak9589"},{"key":"e_1_3_3_12_1","unstructured":"Schulman J. Wolski F. Dhariwal P. Radford A. Klimov O. (2017). Proximal policy optimization algorithms. CoRR abs\/1707.06347."},{"key":"e_1_3_3_13_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41593-021-00884-8"},{"key":"e_1_3_3_14_1","doi-asserted-by":"publisher","DOI":"10.1145\/122344.122377"},{"key":"e_1_3_3_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cub.2022.06.046"},{"key":"e_1_3_3_16_1","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.55252"}],"container-title":["Adaptive Behavior"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/10597123251364749","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/10597123251364749","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/10597123251364749","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T16:19:40Z","timestamp":1777393180000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/10597123251364749"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,21]]},"references-count":15,"journal-issue":{"issue":"5-6","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["10.1177\/10597123251364749"],"URL":"https:\/\/doi.org\/10.1177\/10597123251364749","relation":{},"ISSN":["1059-7123","1741-2633"],"issn-type":[{"value":"1059-7123","type":"print"},{"value":"1741-2633","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,21]]}}}