Functional connectivity in in vitro neuronal assemblies

Poli, Daniele; Pastore, Vito Paolo; Massobrio, Paolo

doi:10.3389/fncir.2015.00057

Cited by 96 publications

(89 citation statements)

References 75 publications

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“…There was striking correlation with culture development stage (age), which was evident particularly in early weeks when new connections are developed rapidly; compared to previous observations in this area predicated on other network topology measures, these results suggest that quasi-idempotence has remarkable sensitivity to the emergent organization exhibited by these networks. 33…”

Section: B Neural Cultures On Multi-electrode Arraysmentioning

confidence: 99%

Self-similarity and quasi-idempotence in neural networks and related dynamical systems

Minati

Winkel

Bifone

et al. 2017

Chaos: An Interdisciplinary Journal of Nonlinear Science

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Self-similarity across length scales is pervasively observed in natural systems. Here, we investigate topological self-similarity in complex networks representing diverse forms of connectivity in the brain and some related dynamical systems, by considering the correlation between edges directly connecting any two nodes in a network and indirect connection between the same via all triangles spanning the rest of the network. We note that this aspect of self-similarity, which is distinct from hierarchically nested connectivity (coarse-grain similarity), is closely related to idempotence of the matrix representing the graph. We introduce two measures, ι(1) and ι(∞), which represent the element-wise correlation coefficients between the initial matrix and the ones obtained after squaring it once or infinitely many times, and term the matrices which yield large values of these parameters "quasi-idempotent". These measures delineate qualitatively different forms of "shallow" and "deep" quasi-idempotence, which are influenced by nodal strength heterogeneity. A high degree of quasi-idempotence was observed for partially synchronized mean-field Kuramoto oscillators with noise, electronic chaotic oscillators, and cultures of dissociated neurons, wherein the expression of quasi-idempotence correlated strongly with network maturity. Quasi-idempotence was also detected for macro-scale brain networks representing axonal connectivity, synchronization of slow activity fluctuations during idleness, and co-activation across experimental tasks, and preliminary data indicated that quasi-idempotence of structural connectivity may decrease with ageing. This initial study highlights that the form of network self-similarity indexed by quasi-idempotence is detectable in diverse dynamical systems, and draws attention to it as a possible basis for measures representing network "collectivity" and pattern formation.

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Section: B Neural Cultures On Multi-electrode Arraysmentioning

confidence: 99%

Self-similarity and quasi-idempotence in neural networks and related dynamical systems

Minati

Winkel

Bifone

et al. 2017

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…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 more rarely in vivo 12 .Here, we propose to fully embrace a temporal network perspective when describing dynamic information sharing within and between cell assemblies. Concretely, we analyze high-density electrophysiological recordings in the hippocampus and medial entorhinal cortex of the rat, allowing to follow in parallel the spiking activity of several tens of single units across different laminar locations in different brain regions.…”

mentioning

confidence: 99%

Dynamic core-periphery structure of information sharing networks in entorhinal cortex and hippocampus

Pedreschi

Bernard

Clawson

et al. 2020

Network Neuroscience

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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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“…It is well known that a neural network cultures form structures with the small-world network topology [11,37]. Growth processes in neural networks depend on the network activity and lead to the network topology which depends on time.…”

Section: Discussionmentioning

confidence: 99%

Emergence of the small-world architecture in neural networks by activity dependent growth

Gafarov

2016

Physica A: Statistical Mechanics and its Applications

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Functional connectivity in in vitro neuronal assemblies

Cited by 96 publications

References 75 publications

Self-similarity and quasi-idempotence in neural networks and related dynamical systems

Self-similarity and quasi-idempotence in neural networks and related dynamical systems

Dynamic core-periphery structure of information sharing networks in entorhinal cortex and hippocampus

Emergence of the small-world architecture in neural networks by activity dependent growth

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