Abstract

In vitro electrophysiological investigation of neural activity at a network level holds tremendous potential for elucidating underlying features of brain function (and dysfunction). In standard neural network modelling systems, however, the fundamental three-dimensional (3D) character of the brain is a largely disregarded feature. This widely applied neuroscientific strategy affects several aspects of the structure–function relationships of the resulting networks, altering network connectivity and topology, ultimately reducing the translatability of the results obtained. As these model systems increase in popularity, it becomes imperative that they capture, as accurately as possible, fundamental features of neural networks in the brain, such as small-worldness. In this report, we combine in vitro neural cell culture with a biologically compatible scaffolding substrate, surface-grafted polymer particles (PPs), to develop neural networks with 3D topology. Furthermore, we investigate their electrophysiological network activity through the use of 3D multielectrode arrays. The resulting neural network activity shows emergent behaviour consistent with maturing neural networks capable of performing computations, i.e. activity patterns suggestive of both information segregation (desynchronized single spikes and local bursts) and information integration (network spikes). Importantly, we demonstrate that the resulting PP-structured neural networks show both structural and functional features consistent with small-world network topology.

Highlights

  • Combining in vitro neural network models with tools for electrophysiological investigation is an established approach for exploring the emerging activity and function of neural networks

  • The neural networks were positively immunolabelled with markers for mature axons as well as synaptic vesicles, post-synaptic elements (PSD95), calcium/calmodulin protein-dependent kinase-ΙΙ (CAMK2), which is involved in neurotransmitter secretion, synaptic connectivity and long-term potentiation, and GFAP

  • It is imperative that the basic characteristics of these widely applied in vitro neural network models capture fundamental structural and functional features of neural networks in the brain as accurately as possible

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Summary

Introduction

Combining in vitro neural network models with tools for electrophysiological investigation is an established (modelling) approach for exploring the emerging activity and function of neural networks. Points towards a prominent activity feature emerging in these in vitro neural networks that is largely incompatible with the activity of the brain, namely highly synchronized activity [1,2,3]. This discrepancy limits the relatability and the potential information that can be gained from this otherwise valuable approach. A highly interdependent nature of structure and function in the neural networks of the brain has been uncovered [4,5,6], which implies that a more realistic topology may need to be recapitulated in our standard modelling systems if they are to produce networks with activity and function traits more relatable to those seen in the brain

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