Neural computation is associated with the emergence, reconfiguration and dissolution of cell assemblies in the context of varying oscillatory states. Here, we describe the complex spatio-temporal dynamics of cell assemblies through temporal network formalism. We use a sliding window approach to extract sequences of networks of information sharing among single units in hippocampus and enthorinal cortex during anesthesia and study how global and node-wise functional connectivity properties evolve along time and as a function of changing global brain state (theta vs slow-wave oscillations). First, we find that information sharing networks display, at any time, a core-periphery structure in which an integrated core of more tightly functionally interconnected units link to more loosely connected network leaves. However the units participating to the core or to the periphery substantially change across time-windows, with units entering and leaving the core in a smooth way. Second, we find that discrete network states can be defined on top of this continuously ongoing liquid core-periphery reorganization. Switching between network states results in a more abrupt modification of the units belonging to the core and is only loosely linked to transitions between global oscillatory states. Third, we characterize different styles of temporal connectivity that cells can exhibit within each state of the sharing network. While inhibitory cells tend to be central, we show that, otherwise, anatomical localization only poorly influences the patterns of temporal connectivity of the different cells. Furthermore, cells can change temporal connectivity style when the network changes state. Altogether, these findings reveal that the sharing of information mediated by the intrinsic dynamics of hippocampal and enthorinal cortex cell assemblies have a rich spatiotemporal structure, which could not have been identified by more conventional time-or state-averaged analyses of functional connectivity. arXiv:2001.06371v1 [q-bio.NC] 17 Jan 2020 between individuals 16, 17 , multiple temporal and structural scales [19][20][21] , and a rich array of intrinsically dynamical structures that could not be unveiled within a static network framework [22][23][24][25] . Taking into account temporality has moreover been shown to have a strong impact in processes taking place on networks, in particular the propagation of diseases or of information 18, 26, 27 . In the neuroscience field, emphasis have been recently put on the need to upgrade "connectomics" into "chronnectomics", to disentangle temporal variability from inter-subject and intercohort differences and thus achieve a superior biomarking performance 28, 29 . However a majority of dynamic network studies have been so far considering large-scale brain-wide networks of interregional connectivity --see, e.g. 30 for a study of link burstiness-and only fewer have addressed the dynamics of information sharing networks at the level of micro-circuits, often in vitro or in silico [31][32][33] and even mor...

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