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Here, we describe the major effective connectivity between the hippocampal memory system and the anterior temporal lobe (ATL) semantic memory system in humans. Then, we present and model a theory of how semantic representations may be formed in the human ATL using slow associative learning in semantic attractor networks that receive inputs from the hippocampal episodic memory system. The hypothesis is that if one category of semantic representations is being processed for several seconds, then a slow short-term memory trace associative biologically plausible learning rule will enable all the components during that time to be associated together in a semantic attractor network. This benefits from the binding of components provided by the hippocampal episodic memory system. The theory is modeled in a four-layer network for view-invariant visual object recognition, followed by a semantic attractor network layer that utilizes a temporal trace associative learning rule to form semantic categories based on the inputs that occur close together in time, using inputs from the hippocampal system or from the world.<\/jats:p>","DOI":"10.1093\/cercor\/bhaf107","type":"journal-article","created":{"date-parts":[[2025,5,10]],"date-time":"2025-05-10T12:16:51Z","timestamp":1746879411000},"source":"Crossref","is-referenced-by-count":12,"title":["Slow semantic learning in the cerebral cortex, and its relation to the hippocampal episodic memory system"],"prefix":"10.1093","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3025-1292","authenticated-orcid":false,"given":"Edmund T","family":"Rolls","sequence":"first","affiliation":[{"name":"Oxford Centre for Computational Neuroscience , Oxford ,","place":["United Kingdom"]},{"name":"Department of Computer 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