Connectivity and network burst properties of in-vitro neuronal networks induced by a clustered structure with alginate hydrogel patterning

Lee, Hyungsub; Yi, Gwan‐Su; Nam, Yoonkey

doi:10.1007/s13534-023-00289-5

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…Modular networks in vitro exhibit integration and segregation activity similar to brain networks. This property has been demonstrated by different groups when studying neural networks consisting of two or more modules (up to 60) [ 32 , 37 , 38 ]. Neural networks in vitro generate spontaneous bursting activity, which can propagate throughout the whole modular network or localize within one module indicating the properties of integration or segregation.…”

Section: Introductionmentioning

confidence: 91%

The Profile of Network Spontaneous Activity and Functional Organization Interplay in Hierarchically Connected Modular Neural Networks In Vitro

Pigareva,

Gladkov,

Kolpakov

et al. 2024

Micromachines

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Modern microtechnology methods are widely used to create neural networks on a chip with a connection architecture demonstrating properties of modularity and hierarchy similar to brain networks. Such in vitro networks serve as a valuable model for studying the interplay of functional architecture within modules, their activity, and the effectiveness of inter-module interaction. In this study, we use a two-chamber microfluidic platform to investigate functional connectivity and global activity in hierarchically connected modular neural networks. We found that the strength of functional connections within the module and the profile of network spontaneous activity determine the effectiveness of inter-modular interaction and integration activity in the network. The direction of intermodular activity propagation configures the different densities of inhibitory synapses in the network. The developed microfluidic platform holds the potential to explore function-structure relationships and efficient information processing in two- or multilayer neural networks, in both healthy and pathological states.

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Section: Introductionmentioning

confidence: 91%