Statistical complexity and connectivity relationship in cultured neural networks

Tlaie, A.; Ballesteros-Esteban, L.M.; Leyva, I.; Sendiña‐Nadal, Irene

doi:10.1016/j.chaos.2018.12.027

Cited by 12 publications

(14 citation statements)

References 40 publications

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“…Specifically, we investigate how the number of links (i.e., the degree k of a neuron) affects the IBI sequence. In networks of coupled oscillators it was recently found, numerically and experimentally [42,43], that there is a relation between the dynamical complexity and the degree: nodes with higher degree were found to have lower levels of complexity.…”

Section: Resultsmentioning

confidence: 99%

Symbolic analysis of bursting dynamical regimes of Rulkov neural networks

Budzinski

Lopes

Masoller

2020

Preprint

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Neurons modeled by the Rulkov map display a variety of dynamic regimes that include tonic spikes and chaotic bursting. Here we study an ensemble of bursting neurons coupled with the Watts-Strogatz small-world topology. We characterize the sequences of bursts using the symbolic method of time-series analysis known as ordinal analysis, which detects nonlinear temporal correlations. We show that the probabilities of the different symbols distinguish different dynamical regimes, which depend on the coupling strength and the network topology. These regimes have different spatio-temporal properties that can be visualized with raster plots.

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Section: Resultsmentioning

confidence: 99%

Symbolic analysis of bursting dynamical regimes of Rulkov neural networks

Budzinski

Lopes

Masoller

2020

Preprint

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“…This issue was partially tackled in Refs. 19,20 , where a strong correlation between degree k and complexity C D allowed establishing a node hierarchy. However, given the present novel results revealing the important role played by the nominal nodal dynamics, we extend our present study to larger networks.…”

Section: Network Of R öSsler Oscillatorsmentioning

confidence: 99%

“…The result is then ensemble averaged over 10 network realizations. Along the route to synchronization, the k-classes synchronize hierarchically to the mean field before all of them lock at the critical coupling (d c = 0.37 for a = 0.26 and d c = 0.52 for a = 0.31), 19,48,49 therefore existing node differentiation also in heterogeneous networks. While the phase synchronization S k is monotonously increasing for most of the coupling range and classes, the weakly coupled regime presents anomalous synchronization 50,51 over which most of the S k are below the basal, uncoupled level.…”

Section: Network Of R öSsler Oscillatorsmentioning

confidence: 99%

“…Recently, it has been shown that the degree of complexity of the node dynamics is strongly dependent on the connectivity; in particular, the local connectedness appears to confer considerable node differentiation hallmarks, albeit following mechanisms that are intricate and delineate a nonmonotonic effect. [18][19][20] These ensemble fingerprints in the node dynamics can be used to infer the network statistical description, or even the detailed connectivity in certain conditions. 21 By node differentiation it is here meant as the dynamical departure of an isolated node due to the interaction with its neighborhood, being more pronounced in networks with nodes having degree heterogeneity.…”

Section: Introductionmentioning

confidence: 99%

“…21 By node differentiation it is here meant as the dynamical departure of an isolated node due to the interaction with its neighborhood, being more pronounced in networks with nodes having degree heterogeneity. In particular, a relatively large range of coupling strengths over which nodes with a higher degree have a less complex dynamics has been reported, 19 ex-plaining the low complexity observed in hubs of functional brain networks. 22 However, the reverse situation, the existence of an even lower range of coupling strengths where high degree nodes are more complex than lower degree ones, remains under study.…”

Section: Introductionmentioning

confidence: 99%

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Node differentiation dynamics along the route to synchronization in complex networks

Letellier,

Sendiña-Nadal,

Minati

et al. 2021

Preprint

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Synchronization has been the subject of intense research during decades mainly focused on determining the structural and dynamical conditions driving a set of interacting units to a coherent state globally stable. However, little attention has been paid to the description of the dynamical development of each individual networked unit in the process towards the synchronization of the whole ensemble. In this paper, we show how in a network of identical dynamical systems, nodes belonging to the same degree class differentiate in the same manner visiting a sequence of states of diverse complexity along the route to synchronization independently on the global network structure. In particular, we observe, just after interaction starts pulling orbits from the initially uncoupled attractor, a general reduction of the complexity of the dynamics of all units being more pronounced in those with higher connectivity. In the weak coupling regime, when synchronization starts to build up, there is an increase in the dynamical complexity whose maximum is achieved, in general, first in the hubs due to their earlier synchronization with the mean field. For very strong coupling, just before complete synchronization, we found a hierarchical dynamical differentiation with lower degree nodes being the ones exhibiting the largest complexity departure. We unveil how this differentiation route holds for several models of nonlinear dynamics including toroidal chaos and how it depends on the coupling function. This study provides new insights to understand better strategies for network identification and control or to devise effective methods for network inference.

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Neuronal circuits on a chip for biological network monitoring

Herreros

Ballesteros-Esteban

Heras

et al. 2021

Biotechnology Journal

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Cultured neuronal networks (CNNs) are a robust model to closely investigate neuronal circuits’ formation and monitor their structural properties evolution. Typically, neurons are cultured in plastic plates or, more recently, in microfluidic platforms with potentially a wide variety of neuroscience applications. As a biological protocol, cell culture integration with a microfluidic system provides benefits such as accurate control of cell seeding area, culture medium renewal, or lower exposure to contamination. The objective of this report is to present a novel neuronal network on a chip device, including a chamber, fabricated from PDMS, vinyl and glass connected to a microfluidic platform to perfuse the continuous flow of culture medium. Network growth is compared in chips and traditional Petri dishes to validate the microfluidic chip performance. The network assessment is performed by computing relevant topological measures like the number of connected neurons, the clustering coefficient, and the shortest path between any pair of neurons throughout the culture's life. The results demonstrate that neuronal circuits on a chip have a more stable network structure and lifespan than developing in conventional settings, and therefore this setup is an advantageous alternative to current culture methods. This technology could lead to challenging applications such as batch drug testing of in vitro cell culture models. From the engineering perspective, a device's advantage is the chance to develop custom designs more efficiently than other microfluidic systems.

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Statistical complexity and connectivity relationship in cultured neural networks

Cited by 12 publications

References 40 publications

Symbolic analysis of bursting dynamical regimes of Rulkov neural networks

Symbolic analysis of bursting dynamical regimes of Rulkov neural networks

Node differentiation dynamics along the route to synchronization in complex networks

Neuronal circuits on a chip for biological network monitoring

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