article published yes Heloisa Chiossi author 2BBA502C-F248-11E8-B48F-1D18A9856A870009-0004-2973-278X Michele Nardin author 30BD0376-F248-11E8-B48F-1D18A9856A870000-0001-8849-6570 Gašper Tkačik author 3D494DCA-F248-11E8-B48F-1D18A9856A870000-0002-6699-1455 Jozsef L Csicsvari author 3FA14672-F248-11E8-B48F-1D18A9856A870000-0002-5193-4036 GaTk department JoCs department International IST Doctoral Program project A key feature of biological and artificial neural networks is the progressive refinement of their neural representations with experience. In neuroscience, this fact has inspired several recent studies in sensory and motor systems. However, less is known about how higher associational cortical areas, such as the hippocampus, modify representations throughout the learning of complex tasks. Here, we focus on associative learning, a process that requires forming a connection between the representations of different variables for appropriate behavioral response. We trained rats in a space-context associative task and monitored hippocampal neural activity throughout the entire learning period, over several days. This allowed us to assess changes in the representations of context, movement direction, and position, as well as their relationship to behavior. We identified a hierarchical representational structure in the encoding of these three task variables that was preserved throughout learning. Nevertheless, we also observed changes at the lower levels of the hierarchy where context was encoded. These changes were local in neural activity space and restricted to physical positions where context identification was necessary for correct decision-making, supporting better context decoding and contextual code compression. Our results demonstrate that the hippocampal code not only accommodates hierarchical relationships between different variables but also enables efficient learning through minimal changes in neural activity space. Beyond the hippocampus, our work reveals a representation learning mechanism that might be implemented in other biological and artificial networks performing similar tasks. https://research-explorer.ista.ac.at/download/19453/19454/2025_PNAS_Chiossi.pdf application/pdfno National Academy of Sciences2025 eng 0027-8424 1091-6490 40063792 00145949950000110.1073/pnas.2417025122 12211 https://research-explorer.ista.ac.at/record/18991 https://github.com/hchiossi/hpc-hierarchy Chiossi HSC, Nardin M, Tkačik G, Csicsvari JL. Learning reshapes the hippocampal representation hierarchy. <i>Proceedings of the National Academy of Sciences</i>. 2025;122(11). doi:<a href="https://doi.org/10.1073/pnas.2417025122">10.1073/pnas.2417025122</a> H.S.C. Chiossi, M. Nardin, G. Tkačik, J.L. Csicsvari, Proceedings of the National Academy of Sciences 122 (2025). Chiossi HSC, Nardin M, Tkačik G, Csicsvari JL. 2025. Learning reshapes the hippocampal representation hierarchy. Proceedings of the National Academy of Sciences. 122(11), e2417025122. Chiossi, Heloisa S. C., Michele Nardin, Gašper Tkačik, and Jozsef L Csicsvari. “Learning Reshapes the Hippocampal Representation Hierarchy.” <i>Proceedings of the National Academy of Sciences</i>. National Academy of Sciences, 2025. <a href="https://doi.org/10.1073/pnas.2417025122">https://doi.org/10.1073/pnas.2417025122</a>. H. S. C. Chiossi, M. Nardin, G. Tkačik, and J. L. Csicsvari, “Learning reshapes the hippocampal representation hierarchy,” <i>Proceedings of the National Academy of Sciences</i>, vol. 122, no. 11. National Academy of Sciences, 2025. Chiossi, H. S. C., Nardin, M., Tkačik, G., &#38; Csicsvari, J. L. (2025). Learning reshapes the hippocampal representation hierarchy. <i>Proceedings of the National Academy of Sciences</i>. National Academy of Sciences. <a href="https://doi.org/10.1073/pnas.2417025122">https://doi.org/10.1073/pnas.2417025122</a> Chiossi, Heloisa S. C., et al. “Learning Reshapes the Hippocampal Representation Hierarchy.” <i>Proceedings of the National Academy of Sciences</i>, vol. 122, no. 11, e2417025122, National Academy of Sciences, 2025, doi:<a href="https://doi.org/10.1073/pnas.2417025122">10.1073/pnas.2417025122</a>. 194532025-03-25T07:38:35Z2025-09-30T11:11:51Z