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The properties of these neurons allow for a biologically plausible learning rule that implements stimulus substitution, utilizing only information available locally at the synapses. We show that the model generates a wide array of conditioning phenomena, and can learn large numbers of associations with an amount of training commensurate with animal experiments, without relying on parameter fine-tuning for each individual experimental task. In contrast, we show that commonly used Hebbian rules fail to learn generic stimulus-stimulus associations with mixed selectivity, and require task-specific parameter fine-tuning. Our framework highlights the importance of multi-compartment neuronal processing in the cortex, and showcases how it might confer cortical animals the evolutionary edge.<\/jats:p>","DOI":"10.1371\/journal.pcbi.1013672","type":"journal-article","created":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T18:58:17Z","timestamp":1763060297000},"page":"e1013672","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["Stimulus-to-stimulus learning in RNNs with cortical inductive biases"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9768-0609","authenticated-orcid":true,"given":"Pantelis","family":"Vafidis","sequence":"first","affiliation":[]},{"given":"Antonio","family":"Rangel","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2025,11,13]]},"reference":[{"issue":"2","key":"pcbi.1013672.ref001","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1901\/jeab.1973.20-163","article-title":"The form of the auto-shaped response with food or water reinforcers","volume":"20","author":"HM Jenkins","year":"1973","journal-title":"J Exp Anal Behav."},{"key":"pcbi.1013672.ref002","doi-asserted-by":"crossref","unstructured":"Dai J, Sun QQ. 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Signsgd: compressed optimisation for non-convex problems. 2018. https:\/\/arxiv.org\/abs\/1802.04434"},{"key":"pcbi.1013672.ref035","doi-asserted-by":"crossref","first-page":"1136010","DOI":"10.3389\/fncom.2023.1136010","article-title":"Learning cortical hierarchies with temporal Hebbian updates","volume":"17","author":"PV Aceituno","year":"2023","journal-title":"Front Comput Neurosci."},{"issue":"7936","key":"pcbi.1013672.ref036","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1038\/s41586-022-05378-6","article-title":"Entorhinal cortex directs learning-related changes in CA1 representations","volume":"611","author":"C Grienberger","year":"2022","journal-title":"Nature."},{"issue":"6","key":"pcbi.1013672.ref037","doi-asserted-by":"crossref","first-page":"398","DOI":"10.1037\/h0034425","article-title":"Latent inhibition","volume":"79","author":"RE Lubow","year":"1973","journal-title":"Psychol Bull."},{"key":"pcbi.1013672.ref038","volume-title":"Fallacies","author":"CL Hamblin","year":"1970"},{"issue":"6","key":"pcbi.1013672.ref039","doi-asserted-by":"crossref","first-page":"860","DOI":"10.1038\/s41593-018-0147-8","article-title":"Prefrontal cortex as a meta-reinforcement learning system","volume":"21","author":"JX Wang","year":"2018","journal-title":"Nat Neurosci."},{"issue":"7474","key":"pcbi.1013672.ref040","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1038\/nature12742","article-title":"Context-dependent computation by recurrent dynamics in prefrontal cortex","volume":"503","author":"V Mante","year":"2013","journal-title":"Nature."},{"issue":"4","key":"pcbi.1013672.ref041","article-title":"Animal intelligence: an experimental study of the associative processes in animals","volume":"2","author":"EL Thorndike","year":"1898","journal-title":"The Psychological Review: Monograph Supplements."},{"key":"pcbi.1013672.ref042","first-page":"64","article-title":"A theory of pavlovian conditioning: variations in the effectiveness of reinforcement and nonreinforcement","author":"RA Rescorla","year":"1972","journal-title":"Current research and theory."},{"issue":"2","key":"pcbi.1013672.ref043","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1037\/0033-295X.88.2.135","article-title":"Toward a modern theory of adaptive networks: expectation and prediction","volume":"88","author":"RS Sutton","year":"1981","journal-title":"Psychological Review."},{"key":"pcbi.1013672.ref044","article-title":"Building machines that learn and think like people","volume":"40","author":"BM Lake","year":"2017","journal-title":"Behav Brain Sci."},{"key":"pcbi.1013672.ref045","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.conb.2016.01.010","article-title":"Why neurons mix: high dimensionality for higher cognition","volume":"37","author":"S Fusi","year":"2016","journal-title":"Curr Opin Neurobiol."},{"key":"pcbi.1013672.ref046","doi-asserted-by":"crossref","DOI":"10.7554\/eLife.69841","article-title":"Learning accurate path integration in ring attractor models of the head direction system","volume":"11","author":"P Vafidis","year":"2022","journal-title":"Elife."},{"key":"pcbi.1013672.ref047","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3389\/fncir.2015.00085","article-title":"Neuromodulated spike-timing-dependent plasticity, and theory of three-factor learning rules","volume":"9","author":"N Fr\u00e9maux","year":"2016","journal-title":"Front Neural Circuits."},{"issue":"4","key":"pcbi.1013672.ref048","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1097\/00004691-199210000-00002","article-title":"The P300 wave of the human event-related potential","volume":"9","author":"TW Picton","year":"1992","journal-title":"J Clin Neurophysiol."},{"issue":"21","key":"pcbi.1013672.ref049","doi-asserted-by":"crossref","first-page":"8209","DOI":"10.1523\/JNEUROSCI.20-21-08209.2000","article-title":"Dopamine release and uptake dynamics within nonhuman primate striatum in vitro","volume":"20","author":"SJ Cragg","year":"2000","journal-title":"J Neurosci."},{"issue":"8","key":"pcbi.1013672.ref050","doi-asserted-by":"crossref","first-page":"455","DOI":"10.1016\/S0166-2236(00)01868-3","article-title":"Synaptic reverberation underlying mnemonic persistent activity","volume":"24","author":"XJ Wang","year":"2001","journal-title":"Trends Neurosci."},{"key":"pcbi.1013672.ref051","first-page":"24213","article-title":"Single-phase deep learning in cortico-cortical networks","volume":"35","author":"W Greedy","year":"2022","journal-title":"Advances in Neural Information Processing Systems."},{"key":"pcbi.1013672.ref052","unstructured":"Kingma D, Ba J. Adam: a method for stochastic optimization. International Conference on Learning Representations. 2014."},{"issue":"5869","key":"pcbi.1013672.ref053","doi-asserted-by":"crossref","first-page":"1543","DOI":"10.1126\/science.1150769","article-title":"Synaptic theory of working memory","volume":"319","author":"G Mongillo","year":"2008","journal-title":"Science."},{"issue":"6355","key":"pcbi.1013672.ref054","doi-asserted-by":"crossref","first-page":"1033","DOI":"10.1126\/science.aan3846","article-title":"Behavioral time scale synaptic plasticity underlies CA1 place fields","volume":"357","author":"KC Bittner","year":"2017","journal-title":"Science."},{"key":"pcbi.1013672.ref055","doi-asserted-by":"crossref","DOI":"10.1017\/CBO9781107447615","volume-title":"Neuronal dynamics","author":"W Gerstner","year":"2014"},{"issue":"2","key":"pcbi.1013672.ref056","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1080\/net.13.2.217.242","article-title":"Self-organizing continuous attractor networks and path integration: one-dimensional models of head direction cells","volume":"13","author":"SM Stringer","year":"2002","journal-title":"Network."}],"updated-by":[{"DOI":"10.1371\/journal.pcbi.1013672","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T00:00:00Z","timestamp":1763510400000}}],"container-title":["PLOS Computational Biology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1013672","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,19]],"date-time":"2025-11-19T18:46:29Z","timestamp":1763577989000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pcbi.1013672"}},"subtitle":[],"editor":[{"given":"Paul","family":"Bays","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2025,11,13]]},"references-count":56,"journal-issue":{"issue":"11","published-online":{"date-parts":[[2025,11,13]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pcbi.1013672","relation":{},"ISSN":["1553-7358"],"issn-type":[{"value":"1553-7358","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,11,13]]}}}