Abstract
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 spatiotemporal 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 entorhinal cortex during anesthesia and study how global and node-wise functional connectivity properties evolve through 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 links 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 entorhinal 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.
Highlights
Since its early definitions (Abeles, 1982; Hebb, 1949), the notion of cell assembly, loosely defined as a group of neurons with coordinated firing within a local or distributed circuit, has been associated with information processing
We show that the temporal network framework provides a natural and effective language to rigorously describe the rich spatiotemporal patterns of information sharing instantiated by cell assembly evolution
Based on the data segments in each of three windows centered at times ta, tb, and tc, we extract an N × N matrix for each time window, where N is the number of neurons and in which the element (i, j) corresponds to the shared information between nodes i and j
Summary
Since its early definitions (Abeles, 1982; Hebb, 1949), the notion of cell assembly, loosely defined as a group of neurons with coordinated firing within a local or distributed circuit, has been associated with information processing. Cell assemblies at the level of neuronal microcircuits—and, in the hippocampal formation, involved in spatial navigation and episodic memory (Buzsáki & Moser, 2013)—have been most often characterized in terms of sets of nodes frequently coactivating in time (Mao, Hamzei-Sichani, Aronov, Froemke, & Yuste, 2001; Miller, Ayzenshtat, Carrillo-Reid, & Yuste, 2014) or repeatedly activating in sequences (Ikegaya et al, 2004; Malvache, Reichinnek, Villette, Haimerl, & Cossart, 2016) Such “static” catalogues of patterns of firing partially fail at highlighting that the temporally coordinated firing of nodes gives rise to a dynamics of functional links (Aertsen, Gerstein, Habib, & Palm, 1989; Bonifazi et al, 2009; Clawson et al, 2019), that is, to a temporal network (Holme, 2015; Holme & Saramäki, 2012; Masuda & Holme, 2017)
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