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O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "BHYS3QUL", "version": 90095, "parentItem": "F678FVP4", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2026-06-13T08:47:17Z", "url": "https://www.biorxiv.org/content/biorxiv/early/2025/03/10/2025.03.07.642117.full.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Elston and Wallis - 2025 - Frontostriatal dynamics of cognitive control.pdf", "md5": "fc41fd936f912992fd25c27ac958dd49", "mtime": 1781340437181, "tags": [], "relations": {}, "dateAdded": "2026-06-13T08:47:17Z", "dateModified": "2026-06-13T08:47:17Z" } }, { "key": "F678FVP4", "version": 90093, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/F678FVP4", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/F678FVP4", "type": "text/html" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "creatorSummary": "Elston and Wallis", "parsedDate": "2025-03-10", "numChildren": 1 }, "data": { "key": "F678FVP4", "version": 90093, "itemType": "preprint", "title": "Frontostriatal dynamics of cognitive control", "creators": [ { "creatorType": "author", "firstName": "Thomas W.", "lastName": "Elston" }, { "creatorType": "author", "firstName": "Joni D.", "lastName": "Wallis" } ], "abstractNote": "A central tenet of cognitive psychology is the existence of two distinct modes of processing: the fast, automatic system and the slow, deliberative system 1,2. The prefrontal cortex is thought to control the balance between these two systems, allowing automatic processing where possible, but stepping in when deliberative processes are required, such as in novel, uncertain, or conflicting situations 3–6. Despite the central importance of this idea in cognitive neuroscience, there is little understanding of how this control process is implemented. We used Neuropixel recordings in rhesus macaques to decode neural activity and reveal the cognitive processes in prefrontal cortex and striatum during automatic and deliberative decisions. During automatic decisions, the chosen option was decoded simultaneously from both areas. During deliberative choices, the prefrontal cortex serially considered multiple options while neither could be decoded from the striatum. This suppression occurred via an increase in alpha band coherence between the two structures and scaled with the volatility of prefrontal representations. Our findings reveal a specific neural mechanism that may explain how frontostriatal circuits implement cognitive control and provide a novel mechanism by which to treat disorders of behavioral control.", "genre": "", "repository": "bioRxiv", "archiveID": "", "place": "", "date": "2025-03-10", "series": "", "seriesNumber": "", "DOI": "10.1101/2025.03.07.642117", "citationKey": "ElstonWallis25a", "url": "https://www.biorxiv.org/content/10.1101/2025.03.07.642117v1", "accessDate": "2026-06-13T08:47:14Z", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "bioRxiv", "callNumber": "", "rights": "© 2025, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution-NoDerivs 4.0 International), CC BY-ND 4.0, as described at http://creativecommons.org/licenses/by-nd/4.0/", "extra": "Pages: 2025.03.07.642117\nSection: New Results", "tags": [], "collections": [], "relations": {}, "dateAdded": "2026-06-13T08:47:14Z", "dateModified": "2026-06-13T08:47:14Z" } }, { "key": "4H6HF2ML", "version": 90091, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/4H6HF2ML", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/4H6HF2ML", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/340666/items/VWEUIVKH", "type": "application/json" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. 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O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "creatorSummary": "Elston and Wallis", "parsedDate": "2025-02", "numChildren": 1 }, "data": { "key": "VWEUIVKH", "version": 90089, "itemType": "journalArticle", "title": "Context-dependent decision-making in the primate hippocampal–prefrontal circuit", "creators": [ { "creatorType": "author", "firstName": "Thomas W.", "lastName": "Elston" }, { "creatorType": "author", "firstName": "Joni D.", "lastName": "Wallis" } ], "abstractNote": "What is good in one scenario may be bad in another. Despite the ubiquity of such contextual reasoning in everyday choice, how the brain flexibly uses different valuation schemes across contexts remains unknown. We addressed this question by monitoring neural activity from the hippocampus (HPC) and orbitofrontal cortex (OFC) of two monkeys performing a state-dependent choice task. We found that HPC neurons encoded state information as it became available and then, at the time of choice, relayed this information to the OFC via theta synchronization. During choice, the OFC represented value in a state-dependent manner; many OFC neurons uniquely coded for value in only one state but not the other. This suggests a functional dissociation whereby the HPC encodes contextual information that is broadcast to the OFC via theta synchronization to select a state-appropriate value subcircuit, thereby allowing for contextual reasoning in value-based choice.", "publicationTitle": "Nature Neuroscience", "publisher": "Nature Publishing Group", "place": "", "date": "2025-02", "volume": "28", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "374-382", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Nat Neurosci", "DOI": "10.1038/s41593-024-01839-5", "citationKey": "ElstonWallis25", "url": "https://www.nature.com/articles/s41593-024-01839-5", "accessDate": "2026-06-13T08:41:07Z", "PMID": "", "PMCID": "", "ISSN": "1546-1726", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "www.nature.com", "callNumber": "", "rights": "2025 The Author(s)", "extra": "", "tags": [ { "tag": "Decision", "type": 1 }, { "tag": "Neural circuits", "type": 1 }, { "tag": "Neurophysiology", "type": 1 }, { "tag": "Population dynamics", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2026-06-13T08:41:07Z", "dateModified": "2026-06-13T08:41:07Z" } }, { "key": "K4GLU5VI", "version": 90086, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/K4GLU5VI", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/K4GLU5VI", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/340666/items/WVFDMXTE", "type": "application/json" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. 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O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "HNS5AUCG", "version": 90085, "parentItem": "WVFDMXTE", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2026-06-13T08:38:44Z", "url": "https://direct.mit.edu/imag/article-pdf/doi/10.1162/IMAG.a.963/2557430/imag.a.963.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Zheng et al. - 2025 - Abstract rule generalization for composing novel meaning recruits a frontoparietal control network.pdf", "md5": "4ed0ee0eb17c685fb25b3c89e9162c08", "mtime": 1781339924399, "tags": [], "relations": {}, "dateAdded": "2026-06-13T08:38:44Z", "dateModified": "2026-06-13T08:38:44Z" } }, { "key": "WVFDMXTE", "version": 90082, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/WVFDMXTE", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/WVFDMXTE", "type": "text/html" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "creatorSummary": "Zheng et al.", "parsedDate": "2025-10-29", "numChildren": 2 }, "data": { "key": "WVFDMXTE", "version": 90082, "itemType": "journalArticle", "title": "Abstract rule generalization for composing novel meaning recruits a frontoparietal control network", "creators": [ { "creatorType": "author", "firstName": "Xiaochen Y.", "lastName": "Zheng" }, { "creatorType": "author", "firstName": "Mona M.", "lastName": "Garvert" }, { "creatorType": "author", "firstName": "Hanneke E. M.", "lastName": "den Ouden" }, { "creatorType": "author", "firstName": "Lisa I.", "lastName": "Horstman" }, { "creatorType": "author", "firstName": "David", "lastName": "Richter" }, { "creatorType": "author", "firstName": "Roshan", "lastName": "Cools" } ], "abstractNote": "The ability to generalize previously learned knowledge to novel situations is crucial for adaptive behavior, representing a form of cognitive flexibility that is particularly relevant in language. Humans excel at combining linguistic building blocks to infer the meanings of novel compositional words, such as “un-reject-able-ish”. The neural mechanisms and representations required for this ability remain unclear. To unravel these, we trained participants on a semi-artificial language in which the meanings of compositional words could be derived from known stems and unknown affixes, using abstract relational structure rules (e.g., “good-kla” which means “bad”, where “-kla” reverses the meaning of the stem word “good”). According to these rules, word meaning depended on the sequential relation between the stem and the affix (i.e., pre- vs. post-stem). During fMRI, participants performed a semantic priming task, with novel compositional words as either sequential order congruent (e.g., “short-kla”) or incongruent primes (e.g., “kla-short”), and real words serving as targets that were synonyms of the composed meaning of the congruent primes (e.g., “long”). Our results show that the compositional process engaged a broad temporoparietal network, while representations of composed word meaning were localized in a more circumscribed left-lateralized language network. Strikingly, newly composed meanings were decodable already at the time of the prime in a way that could not be accounted for representations of the prime words themselves. Finally, we found that the composition process recruited abstract rule representations in a bilateral frontoparietal network, in contrast to our preregistered prediction of a medial prefrontal-hippocampal network. These results support the hypothesis that people activate a bilateral frontoparietal circuitry for compositional inference and generalization in language.", "publicationTitle": "Imaging Neuroscience", "publisher": "", "place": "", "date": "2025-10-29", "volume": "3", "issue": "", "section": "", "partNumber": "", "partTitle": "", "pages": "IMAG.a.963", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1162/IMAG.a.963", "citationKey": "ZhengGarvertdenOudenEtAl25", "url": "https://doi.org/10.1162/IMAG.a.963", "accessDate": "2026-06-13T08:38:42Z", "PMID": "", "PMCID": "", "ISSN": "2837-6056", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Silverchair", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2026-06-13T08:38:42Z", "dateModified": "2026-06-13T08:38:42Z" } }, { "key": "MZ9WQ7K4", "version": 90080, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/MZ9WQ7K4", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/MZ9WQ7K4", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/340666/items/K9RLF255", "type": "application/json" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "MZ9WQ7K4", "version": 90080, "parentItem": "K9RLF255", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2026-06-13T08:37:08Z", "url": "https://www.nature.com/articles/s41593-023-01283-x.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Garvert et al. - 2023 - Hippocampal spatio-predictive cognitive maps adaptively guide reward generalization.pdf", "md5": "f86fb5ee445cc21af7ddde8aa5119e00", "mtime": 1781339828216, "tags": [], "relations": {}, "dateAdded": "2026-06-13T08:37:08Z", "dateModified": "2026-06-13T08:37:08Z" } }, { "key": "K9RLF255", "version": 90078, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/K9RLF255", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/K9RLF255", "type": "text/html" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "creatorSummary": "Garvert et al.", "parsedDate": "2023-04", "numChildren": 1 }, "data": { "key": "K9RLF255", "version": 90078, "itemType": "journalArticle", "title": "Hippocampal spatio-predictive cognitive maps adaptively guide reward generalization", "creators": [ { "creatorType": "author", "firstName": "Mona M.", "lastName": "Garvert" }, { "creatorType": "author", "firstName": "Tankred", "lastName": "Saanum" }, { "creatorType": "author", "firstName": "Eric", "lastName": "Schulz" }, { "creatorType": "author", "firstName": "Nicolas W.", "lastName": "Schuck" }, { "creatorType": "author", "firstName": "Christian F.", "lastName": "Doeller" } ], "abstractNote": "The brain forms cognitive maps of relational knowledge—an organizing principle thought to underlie our ability to generalize and make inferences. However, how can a relevant map be selected in situations where a stimulus is embedded in multiple relational structures? Here, we find that both spatial and predictive cognitive maps influence generalization in a choice task, where spatial location determines reward magnitude. Mirroring behavior, the hippocampus not only builds a map of spatial relationships but also encodes the experienced transition structure. As the task progresses, participants’ choices become more influenced by spatial relationships, reflected in a strengthening of the spatial map and a weakening of the predictive map. This change is driven by orbitofrontal cortex, which represents the degree to which an outcome is consistent with the spatial rather than the predictive map and updates hippocampal representations accordingly. Taken together, this demonstrates how hippocampal cognitive maps are used and updated flexibly for inference.", "publicationTitle": "Nature Neuroscience", "publisher": "Nature Publishing Group", "place": "", "date": "2023-04", "volume": "26", "issue": "4", "section": "", "partNumber": "", "partTitle": "", "pages": "615-626", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Nat Neurosci", "DOI": "10.1038/s41593-023-01283-x", "citationKey": "GarvertSaanumSchulzEtAl23", "url": "https://www.nature.com/articles/s41593-023-01283-x", "accessDate": "2026-06-13T08:37:04Z", "PMID": "", "PMCID": "", "ISSN": "1546-1726", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "www.nature.com", "callNumber": "", "rights": "2023 The Author(s)", "extra": "", "tags": [ { "tag": "Decision", "type": 1 }, { "tag": "Hippocampus", "type": 1 }, { "tag": "Learning algorithms", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2026-06-13T08:37:04Z", "dateModified": "2026-06-13T08:37:06Z" } }, { "key": "9LETVP7S", "version": 90076, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/9LETVP7S", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/9LETVP7S", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/340666/items/2Q7EWCLG", "type": "application/json" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "9LETVP7S", "version": 90076, "parentItem": "2Q7EWCLG", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2026-06-13T08:33:29Z", "url": "https://www.biorxiv.org/content/biorxiv/early/2026/05/11/2024.10.11.617652.full.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Baram et al. - 2026 - An abstract relational map emerges in the human medial prefrontal cortex with consolidation.pdf", "md5": "fae4a1e77310db4f4e1af9cd82271775", "mtime": 1781339609015, "tags": [], "relations": {}, "dateAdded": "2026-06-13T08:33:29Z", "dateModified": "2026-06-13T08:33:29Z" } }, { "key": "2Q7EWCLG", "version": 90074, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/2Q7EWCLG", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/2Q7EWCLG", "type": "text/html" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "creatorSummary": "Baram et al.", "parsedDate": "2026-05-11", "numChildren": 1 }, "data": { "key": "2Q7EWCLG", "version": 90074, "itemType": "preprint", "title": "An abstract relational map emerges in the human medial prefrontal cortex with consolidation", "creators": [ { "creatorType": "author", "firstName": "Alon B.", "lastName": "Baram" }, { "creatorType": "author", "firstName": "Hamed", "lastName": "Nili" }, { "creatorType": "author", "firstName": "Ines", "lastName": "Barreiros" }, { "creatorType": "author", "firstName": "Veronika", "lastName": "Samborska" }, { "creatorType": "author", "firstName": "Timothy E. J.", "lastName": "Behrens" }, { "creatorType": "author", "firstName": "Mona M.", "lastName": "Garvert" } ], "abstractNote": "Understanding the structure of a problem, such as the relationships between stimuli, supports fast learning and flexible reasoning. Rodent work has suggested that abstraction of structure away from sensory details occurs over the course of multiple days, in cortex. However, direct evidence of such explicit relational representations in humans is scarce, and it is unclear whether they emerge on similar timescales. Here, we combine a graph-learning paradigm with functional magnetic resonance imaging (fMRI) to look for such a relational map in the human brain. We first trained participants on two associative graphs with the same structure. We then scanned participants twice while they used this knowledge, with several days between scanning sessions. Using fMRI repetition suppression, we found an abstract relational representation in the medial prefrontal cortex (mPFC), that emerged across the two scanning sessions. This finding was also replicated using representational similarity analysis (RSA). These results shed new light on how neural representations organising relational knowledge change with time.", "genre": "", "repository": "bioRxiv", "archiveID": "", "place": "", "date": "2026-05-11", "series": "", "seriesNumber": "", "DOI": "10.1101/2024.10.11.617652", "citationKey": "BaramNiliBarreirosEtAl26", "url": "https://www.biorxiv.org/content/10.1101/2024.10.11.617652v3", "accessDate": "2026-06-13T08:33:25Z", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "bioRxiv", "callNumber": "", "rights": "© 2026, Posted by openRxiv. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/", "extra": "ISSN: 2692-8205\nPages: 2024.10.11.617652\nSection: New Results", "tags": [], "collections": [], "relations": {}, "dateAdded": "2026-06-13T08:33:25Z", "dateModified": "2026-06-13T08:33:27Z" } }, { "key": "2ALPTM3F", "version": 90072, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/2ALPTM3F", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/2ALPTM3F", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/340666/items/KYMA6HIG", "type": "application/json" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. 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O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "creatorSummary": "Doohan et al.", "parsedDate": "2026-06-10", "numChildren": 1 }, "data": { "key": "KYMA6HIG", "version": 90070, "itemType": "preprint", "title": "Structured and flexible representations in medial-frontal cortex support goal-directed navigation", "creators": [ { "creatorType": "author", "firstName": "Peter T.", "lastName": "Doohan" }, { "creatorType": "author", "firstName": "Kristopher T.", "lastName": "Jensen" }, { "creatorType": "author", "firstName": "Yaqing", "lastName": "Chen" }, { "creatorType": "author", "firstName": "Beatriz S.", "lastName": "Godinho" }, { "creatorType": "author", "firstName": "Charles D. G.", "lastName": "Burns" }, { "creatorType": "author", "firstName": "Chongyu", "lastName": "Qin" }, { "creatorType": "author", "firstName": "Josie L.", "lastName": "Emery" }, { "creatorType": "author", "firstName": "Ryan J.", "lastName": "Cini" }, { "creatorType": "author", "firstName": "Mark E.", "lastName": "Walton" }, { "creatorType": "author", "firstName": "Timothy E. J.", "lastName": "Behrens" }, { "creatorType": "author", "firstName": "Thomas E.", "lastName": "Akam" } ], "abstractNote": "Humans and animals plan actions to achieve goals in worlds that are complex and continually changing. While planning is critically dependent on the prefrontal cortex in humans, little is known about its cellular underpinnings. Mechanistic understanding has been limited by a scarcity of controlled animal experiments in which subjects must flexibly plan novel behaviours on every trial. Here we characterise the neural representations and dynamics of mouse medial frontal cortex (mFC) during flexible navigation in structured environments. We trained mice to navigate complex mazes, to goals that changed location on every trial. Optogenetic silencing established that mFC was necessary for efficient navigation. mFC activity was dominated by two factorised components: (i) a structured representation of subjects' position within the maze that formed an efficient code for behavioural trajectories, and (ii) a flexible representation of the shortest path-distance to the current goal. Both representations oscillated within local field potential (LFP) theta cycles, processing from further to closer to the goal at a systematic offset. These data suggest a computation in which mFC evaluates possible futures by their distance-to-goal to update a structured behavioural policy.", "genre": "", "repository": "bioRxiv", "archiveID": "", "place": "", "date": "2026-06-10", "series": "", "seriesNumber": "", "DOI": "10.64898/2026.06.09.729603", "citationKey": "DoohanJensenChenEtAl26", "url": "https://www.biorxiv.org/content/10.64898/2026.06.09.729603v1", "accessDate": "2026-06-13T08:22:37Z", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "en", "libraryCatalog": "bioRxiv", "callNumber": "", "rights": "© 2026, Posted by openRxiv. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/", "extra": "ISSN: 2692-8205\nPages: 2026.06.09.729603\nSection: New Results", "tags": [], "collections": [], "relations": {}, "dateAdded": "2026-06-13T08:22:37Z", "dateModified": "2026-06-13T08:22:39Z" } }, { "key": "36V49XPZ", "version": 90069, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/36V49XPZ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/36V49XPZ", "type": "text/html" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "creatorSummary": "Hopfield and Tank", "parsedDate": "1985", "numChildren": 1 }, "data": { "key": "36V49XPZ", "version": 90069, "itemType": "journalArticle", "title": "\"Neural\" computation of decisions in optimization problems", "creators": [ { "creatorType": "author", "firstName": "J. J.", "lastName": "Hopfield" }, { "creatorType": "author", "firstName": "D. W.", "lastName": "Tank" } ], "abstractNote": "", "publicationTitle": "Biological Cybernetics", "publisher": "", "place": "", "date": "xx 1985", "volume": "52", "issue": "3", "section": "", "partNumber": "", "partTitle": "", "pages": "141-152", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "", "citationKey": "HopfieldTank85", "url": "http://www.ncbi.nlm.nih.gov/pubmed/4027280", "accessDate": "", "PMID": "", "PMCID": "", "ISSN": "", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "#LaTeX" } ], "collections": [], "relations": {}, "dateAdded": "2015-04-01T06:29:02Z", "dateModified": "2026-06-12T17:38:11Z" } }, { "key": "CS48LHLZ", "version": 90067, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/CS48LHLZ", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/CS48LHLZ", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/340666/items/ZSL3E29C", "type": "application/json" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. 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O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "creatorSummary": "Bukovsky and Homma", "parsedDate": "2017-09", "numChildren": 1 }, "data": { "key": "ZSL3E29C", "version": 90065, "itemType": "journalArticle", "title": "An Approach to Stable Gradient-Descent Adaptation of Higher Order Neural Units", "creators": [ { "creatorType": "author", "firstName": "Ivo", "lastName": "Bukovsky" }, { "creatorType": "author", "firstName": "Noriyasu", "lastName": "Homma" } ], "abstractNote": "Stability evaluation of a weight-update system of higher order neural units (HONUs) with polynomial aggregation of neural inputs (also known as classes of polynomial neural networks) for adaptation of both feedforward and recurrent HONUs by a gradient descent method is introduced. An essential core of the approach is based on the spectral radius of a weight-update system, and it allows stability monitoring and its maintenance at every adaptation step individually. Assuring the stability of the weight-update system (at every single adaptation step) naturally results in the adaptation stability of the whole neural architecture that adapts to the target data. As an aside, the used approach highlights the fact that the weight optimization of HONU is a linear problem, so the proposed approach can be generally extended to any neural architecture that is linear in its adaptable parameters.", "publicationTitle": "IEEE Transactions on Neural Networks and Learning Systems", "publisher": "", "place": "", "date": "2017-09", "volume": "28", "issue": "9", "section": "", "partNumber": "", "partTitle": "", "pages": "2022-2034", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1109/TNNLS.2016.2572310", "citationKey": "BukovskyHomma17", "url": "https://ieeexplore.ieee.org/document/7487017", "accessDate": "2026-06-12T16:56:42Z", "PMID": "", "PMCID": "", "ISSN": "2162-2388", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Biological neural networks", "type": 1 }, { "tag": "Gradient descent (GD)", "type": 1 }, { "tag": "Indexes", "type": 1 }, { "tag": "Maintenance engineering", "type": 1 }, { "tag": "Neurons", "type": 1 }, { "tag": "Optimization", "type": 1 }, { "tag": "Stability criteria", "type": 1 }, { "tag": "higher order neural unit (HONU)", "type": 1 }, { "tag": "polynomial neural network", "type": 1 }, { "tag": "spectral radius", "type": 1 }, { "tag": "stability", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2026-06-12T16:56:42Z", "dateModified": "2026-06-12T16:56:42Z" } }, { "key": "JQ3NKWPR", "version": 90063, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/JQ3NKWPR", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/JQ3NKWPR", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/340666/items/T2K6SK6J", "type": "application/json" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. 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O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } } }, "data": { "key": "ZB8Z7KHY", "version": 90057, "parentItem": "W7SZ4GIR", "itemType": "attachment", "linkMode": "imported_url", "title": "Snapshot", "accessDate": "2026-06-12T16:36:16Z", "url": "https://ieeexplore.ieee.org/abstract/document/8409490", "note": "", "contentType": "text/html", "charset": "utf-8", "filename": "8409490.html", "md5": "2a3060135dbe21d1e242f5f63a70869e", "mtime": 1781282176718, "tags": [], "relations": {}, "dateAdded": "2026-06-12T16:36:16Z", "dateModified": "2026-06-12T16:36:16Z" } }, { "key": "W7SZ4GIR", "version": 90055, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/W7SZ4GIR", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/W7SZ4GIR", "type": "text/html" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "creatorSummary": "Gao et al.", "parsedDate": "2019-02", "numChildren": 2 }, "data": { "key": "W7SZ4GIR", "version": 90055, "itemType": "journalArticle", "title": "Dendritic Neuron Model With Effective Learning Algorithms for Classification, Approximation, and Prediction", "creators": [ { "creatorType": "author", "firstName": "Shangce", "lastName": "Gao" }, { "creatorType": "author", "firstName": "Mengchu", "lastName": "Zhou" }, { "creatorType": "author", "firstName": "Yirui", "lastName": "Wang" }, { "creatorType": "author", "firstName": "Jiujun", "lastName": "Cheng" }, { "creatorType": "author", "firstName": "Hanaki", "lastName": "Yachi" }, { "creatorType": "author", "firstName": "Jiahai", "lastName": "Wang" } ], "abstractNote": "An artificial neural network (ANN) that mimics the information processing mechanisms and procedures of neurons in human brains has achieved a great success in many fields, e.g., classification, prediction, and control. However, traditional ANNs suffer from many problems, such as the hard understanding problem, the slow and difficult training problems, and the difficulty to scale them up. These problems motivate us to develop a new dendritic neuron model (DNM) by considering the nonlinearity of synapses, not only for a better understanding of a biological neuronal system, but also for providing a more useful method for solving practical problems. To achieve its better performance for solving problems, six learning algorithms including biogeography-based optimization, particle swarm optimization, genetic algorithm, ant colony optimization, evolutionary strategy, and population-based incremental learning are for the first time used to train it. The best combination of its user-defined parameters has been systemically investigated by using the Taguchi's experimental design method. The experiments on 14 different problems involving classification, approximation, and prediction are conducted by using a multilayer perceptron and the proposed DNM. The results suggest that the proposed learning algorithms are effective and promising for training DNM and thus make DNM more powerful in solving classification, approximation, and prediction problems.", "publicationTitle": "IEEE Transactions on Neural Networks and Learning Systems", "publisher": "", "place": "", "date": "2019-02", "volume": "30", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "601-614", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "", "DOI": "10.1109/TNNLS.2018.2846646", "citationKey": "GaoZhouWangEtAl19", "url": "https://ieeexplore.ieee.org/abstract/document/8409490", "accessDate": "2026-06-12T16:36:14Z", "PMID": "", "PMCID": "", "ISSN": "2162-2388", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "IEEE Xplore", "callNumber": "", "rights": "", "extra": "", "tags": [ { "tag": "Approximation", "type": 1 }, { "tag": "Approximation algorithms", "type": 1 }, { "tag": "Function approximation", "type": 1 }, { "tag": "Neurons", "type": 1 }, { "tag": "Optimization", "type": 1 }, { "tag": "Prediction algorithms", "type": 1 }, { "tag": "Synapses", "type": 1 }, { "tag": "Training", "type": 1 }, { "tag": "brain", "type": 1 }, { "tag": "classification", "type": 1 }, { "tag": "dendritic neuron model (DNM)", "type": 1 }, { "tag": "global learning algorithms", "type": 1 }, { "tag": "prediction", "type": 1 } ], "collections": [], "relations": {}, "dateAdded": "2026-06-12T16:36:14Z", "dateModified": "2026-06-12T16:36:14Z" } }, { "key": "3FGP2ST4", "version": 90052, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/3FGP2ST4", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/3FGP2ST4", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/340666/items/T2K6SK6J", "type": "application/json" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. 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O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "creatorSummary": "Schmitt", "parsedDate": "2002-02-01", "numChildren": 2 }, "data": { "key": "T2K6SK6J", "version": 90049, "itemType": "journalArticle", "title": "On the Complexity of Computing and Learning with Multiplicative Neural Networks", "creators": [ { "creatorType": "author", "firstName": "Michael", "lastName": "Schmitt" } ], "abstractNote": "In a great variety of neuron models, neural inputs are combined using the summing operation. We introduce the concept of multiplicative neural networks that contain units that multiply their inputs instead of summing them and thus allow inputs to interact nonlinearly. The class of multiplicative neural networks comprises such widely known and well-studied network types as higher-order networks and product unit networks.We investigate the complexity of computing and learning for multiplicative neural networks. In particular, we derive upper and lower bounds on the Vapnik-Chervonenkis (VC) dimension and the pseudo-dimension for various types of networks with multiplicative units. As the most general case, we consider feedforward networks consisting of product and sigmoidal units, showing that their pseudo-dimension is bounded from above by a polynomial with the same order of magnitude as the currently best-known bound for purely sigmoidal networks. Moreover, we show that this bound holds even when the unit type, product or sigmoidal, may be learned. Crucial for these results are calculations of solution set components bounds for new network classes. As to lower bounds, we construct product unit networks of fixed depth with super-linear VC dimension.For sigmoidal networks of higher order, we establish polynomial bounds that, in contrast to previous results, do not involve any restriction of the network order. We further consider various classes of higher-order units, also known as sigma-pi units, that are characterized by connectivity constraints. In terms of these, we derive some asymptotically tight bounds.Multiplication plays an important role in both neural modeling of biological behavior and computing and learning with artificial neural networks. We briefly survey research in biology and in applications where multiplication is considered an essential computational element. The results we present here provide new tools for assessing the impact of multiplication on the computational power and the learning capabilities of neural networks.", "publicationTitle": "Neural Computation", "publisher": "", "place": "", "date": "2002-02-01", "volume": "14", "issue": "2", "section": "", "partNumber": "", "partTitle": "", "pages": "241-301", "series": "", "seriesTitle": "", "seriesText": "", "journalAbbreviation": "Neural Comput", "DOI": "10.1162/08997660252741121", "citationKey": "Schmitt02", "url": "https://doi.org/10.1162/08997660252741121", "accessDate": "2026-06-12T16:35:19Z", "PMID": "", "PMCID": "", "ISSN": "0899-7667", "archive": "", "archiveLocation": "", "shortTitle": "", "language": "", "libraryCatalog": "Silverchair", "callNumber": "", "rights": "", "extra": "", "tags": [], "collections": [], "relations": {}, "dateAdded": "2026-06-12T16:35:19Z", "dateModified": "2026-06-12T16:35:19Z" } }, { "key": "FN437PPR", "version": 90051, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/FN437PPR", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/FN437PPR", "type": "text/html" }, "up": { "href": "https://api.zotero.org/groups/340666/items/8T5NGQP3", "type": "application/json" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "numChildren": 0 }, "data": { "key": "FN437PPR", "version": 90051, "parentItem": "8T5NGQP3", "itemType": "attachment", "linkMode": "imported_url", "title": "Full Text PDF", "accessDate": "2026-06-12T16:27:58Z", "url": "https://link.springer.com/content/pdf/10.1007%2F978-981-15-2449-3_37.pdf", "note": "", "contentType": "application/pdf", "charset": "", "filename": "Swapna Rekha et al. - 2020 - Pi-Sigma Neural Network Survey of a Decade Progress.pdf", "md5": "a507d5ea0c627a3a81a420e4f1261f21", "mtime": 1781281678547, "tags": [], "relations": {}, "dateAdded": "2026-06-12T16:27:58Z", "dateModified": "2026-06-12T16:27:58Z" } }, { "key": "8T5NGQP3", "version": 90047, "library": { "type": "group", "id": 340666, "name": "CCNLab", "links": { "alternate": { "href": "https://www.zotero.org/groups/ccnlab", "type": "text/html" } } }, "links": { "self": { "href": "https://api.zotero.org/groups/340666/items/8T5NGQP3", "type": "application/json" }, "alternate": { "href": "https://www.zotero.org/groups/ccnlab/items/8T5NGQP3", "type": "text/html" } }, "meta": { "createdByUser": { "id": 2406800, "username": "rcoreilly", "name": "Randall C. O'Reilly", "links": { "alternate": { "href": "https://www.zotero.org/rcoreilly", "type": "text/html" } } }, "creatorSummary": "Swapna Rekha et al.", "parsedDate": "2020", "numChildren": 1 }, "data": { "key": "8T5NGQP3", "version": 90047, "itemType": "conferencePaper", "title": "Pi-Sigma Neural Network: Survey of a Decade Progress", "creators": [ { "creatorType": "author", "firstName": "H.", "lastName": "Swapna Rekha" }, { "creatorType": "author", "firstName": "Janmenjoy", "lastName": "Nayak" }, { "creatorType": "author", "firstName": "H. S.", "lastName": "Behera" }, { "creatorType": "editor", "firstName": "Asit Kumar", "lastName": "Das" }, { "creatorType": "editor", "firstName": "Janmenjoy", "lastName": "Nayak" }, { "creatorType": "editor", "firstName": "Bighnaraj", "lastName": "Naik" }, { "creatorType": "editor", "firstName": "Soumi", "lastName": "Dutta" }, { "creatorType": "editor", "firstName": "Danilo", "lastName": "Pelusi" } ], "abstractNote": "From the past few years due to the rapid development in neural networks, it has been emerging as the most prominent research area in scientific community. To overcome the limitations of neural networks such as large training time and incapability to cope with nonlinear data, researchers have been attracted toward higher-order neural networks. Nonlinear mapping capability of higher-order neural network using fewer number of processing units leads to major enhancement in the productivity and performance of several classification methods. In this paper, an extensive analytical study of a higher-order neural network called Pi-sigma neural network and its variants in various application domains such as classification, prediction, function approximation, pattern recognition has been illustrated. 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