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      Hippocampal and orbitofrontal neurons contribute to complementary aspects of associative structure

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          Abstract

          The ability to establish associations between environmental stimuli is fundamental for higher-order brain functions like state inference and generalization. Both the hippocampus and orbitofrontal cortex (OFC) play pivotal roles in this, demonstrating complex neural activity changes after associative learning. However, how precisely they contribute to representing learned associations remains unclear. Here, we train head-restrained mice to learn four ‘odor-outcome’ sequence pairs composed of several task variables—the past and current odor cues, sequence structure of ‘cue-outcome’ arrangement, and the expected outcome; and perform calcium imaging from these mice throughout learning. Sequence-splitting signals that distinguish between paired sequences are detected in both brain regions, reflecting associative memory formation. Critically, we uncover differential contents in represented associations by examining, in each area, how these task variables affect splitting signal generalization between sequence pairs. Specifically, the hippocampal splitting signals are influenced by the combination of past and current cues that define a particular sensory experience. In contrast, the OFC splitting signals are similar between sequence pairs that share the same sequence structure and expected outcome. These findings suggest that the hippocampus and OFC uniquely and complementarily organize the acquired associative structure.

          Abstract

          By performing calcium imaging on head-fixed mice throughout learning, Lin and Zhou demonstrate that hippocampal and orbitofrontal neurons uniquely and complementarily encode different aspects of learned associations between environmental stimuli.

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          Most cited references60

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          LIBSVM: A library for support vector machines

          LIBSVM is a library for Support Vector Machines (SVMs). We have been actively developing this package since the year 2000. The goal is to help users to easily apply SVM to their applications. LIBSVM has gained wide popularity in machine learning and many other areas. In this article, we present all implementation details of LIBSVM. Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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            Memory, navigation and theta rhythm in the hippocampal-entorhinal system.

            Theories on the functions of the hippocampal system are based largely on two fundamental discoveries: the amnestic consequences of removing the hippocampus and associated structures in the famous patient H.M. and the observation that spiking activity of hippocampal neurons is associated with the spatial position of the rat. In the footsteps of these discoveries, many attempts were made to reconcile these seemingly disparate functions. Here we propose that mechanisms of memory and planning have evolved from mechanisms of navigation in the physical world and hypothesize that the neuronal algorithms underlying navigation in real and mental space are fundamentally the same. We review experimental data in support of this hypothesis and discuss how specific firing patterns and oscillatory dynamics in the entorhinal cortex and hippocampus can support both navigation and memory.
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              What Is a Cognitive Map? Organizing Knowledge for Flexible Behavior

              It is proposed that a cognitive map encoding the relationships between entities in the world supports flexible behavior, but the majority of the neural evidence for such a system comes from studies of spatial navigation. Recent work describing neuronal parallels between spatial and non-spatial behaviors has rekindled the notion of a systematic organization of knowledge across multiple domains. We review experimental evidence and theoretical frameworks that point to principles unifying these apparently disparate functions. These principles describe how to learn and use abstract, generalizable knowledge and suggest that map-like representations observed in a spatial context may be an instance of general coding mechanisms capable of organizing knowledge of all kinds. We highlight how artificial agents endowed with such principles exhibit flexible behavior and learn map-like representations observed in the brain. Finally, we speculate on how these principles may offer insight into the extreme generalizations, abstractions, and inferences that characterize human cognition.

                Author and article information

                Contributors
                jingfeng.zhou@cibr.ac.cn
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                20 June 2024
                20 June 2024
                2024
                : 15
                : 5283
                Affiliations
                [1 ]Academy for Advanced Interdisciplinary Studies, Peking University, ( https://ror.org/02v51f717) Beijing, 100871 China
                [2 ]Chinese Institute for Brain Research, ( https://ror.org/029819q61) Beijing, 102206 China
                Author information
                http://orcid.org/0009-0009-6646-1487
                http://orcid.org/0000-0003-1893-1025
                Article
                49652
                10.1038/s41467-024-49652-9
                11190210
                38902232
                c65d67e3-69a9-411a-ab40-d18d24fbef1b
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 24 August 2023
                : 12 June 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100002855, Ministry of Science and Technology of the People's Republic of China (Chinese Ministry of Science and Technology);
                Award ID: 2022ZD0207500
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100009592, Beijing Municipal Science and Technology Commission;
                Award ID: Z211100002121029
                Award Recipient :
                Categories
                Article
                Custom metadata
                © Springer Nature Limited 2024

                Uncategorized
                classical conditioning,hippocampus,cortex,neural encoding,reward
                Uncategorized
                classical conditioning, hippocampus, cortex, neural encoding, reward

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