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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Integr. Neurosci. | doi: 10.3389/fnint.2019.00054

Multimodal memory components and their long-term dynamics identified in cortical layers II/III but not layer V

  • 1Institut für Computational Neuroscience, Universitätsklinikum Hamburg-Eppendorf, Germany
  • 2School of Life Science and Technology, ShanghaiTech University, China
  • 3School of Life Sciences, Tsinghua University, China
  • 4Research Center for Brain-inspired Intelligence, Institute of Automation (CAS), China
  • 5Department of Health Sciences, College of Health & Rehabilitation Sciences, Sargent College, Boston University, United States

Activity patterns of cerebral cortical regions represent the current environment in which animals receive multi-modal inputs. These patterns are also shaped by the history of previous activity that reflects learned information on past multimodal exposures. We studied the long-term dynamics of cortical activity patterns during the formation of multimodal memories by analysing in vivo high-resolution 2-photon mouse brain imaging data of Immediate Early Gene (IEG) expression, resolved by cortical layers. Strikingly, in superficial layers II/III, the patterns showed similar dynamics across structurally and functionally distinct cortical areas and the consistency of dynamic patterns lasted for one to several days. By contrast, in deep layer V, the activity dynamics varied across different areas, and the current activities were sensitive to the previous activities at different time points depending on the cortical locations, indicating that the information stored in the cortex at different time points was distributed across different cortical areas. These results suggest different roles of superficial and deep layer neurons in the long-term multimodal representation of the environment.

Keywords: cortical dynamics, cortical layers, multimodal learning and memory, 2-photon imaging, Mice

Received: 08 Jun 2019; Accepted: 09 Sep 2019.

Copyright: © 2019 Li, Wang, Xie, Hu, Guan and Hilgetag. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Prof. Ji-Song Guan, School of Life Science and Technology, ShanghaiTech University, Shanghai, 201210, China,
Dr. Claus C. Hilgetag, Institut für Computational Neuroscience, Universitätsklinikum Hamburg-Eppendorf, Hamburg, 20246, Hamburg, Germany,