Abstract
The structured organization of connectivity in neural networks is associated with highly efficient information propagation and processing in the brain, in contrast with disordered homogeneous network architectures. Using microfluidic methods, we engineered modular networks of cultures using dissociated cells with unidirectional synaptic connections formed by asymmetric microchannels. The complexity of the microchannel geometry defined the strength of the synaptic connectivity and the properties of spiking activity propagation. In this study, we developed an experimental platform to study the effects of synaptic plasticity on a network level with predefined locations of unidirectionally connected cellular assemblies using multisite extracellular electrophysiology.
Highlights
Published: 28 May 2021Theoretical and in vivo studies show that the modular structure of neural networks with a heterogeneous synaptic architecture provides energy-efficient propagation of spiking patterns, which enables optimal information processing [1,2,3]
Neurites from the Source module grew into the large section (400 μm) in the Octopus channels for 2.5 ± 0.5 days, and in the Fish channels for 4 ± 0.5 days
We found that the mean post-stimulus time histogram (PSTH) in the Target module of the Fish cultures was equal to 5.3 ± 15, which was lower than that obtained in the Source module (Mann–Whitney Rank Sum Test, p < 0.001)
Summary
Published: 28 May 2021Theoretical and in vivo studies show that the modular structure of neural networks with a heterogeneous synaptic architecture provides energy-efficient propagation of spiking patterns, which enables optimal information processing [1,2,3]. The guidance of axon growth through asymmetric microchannels can be used to form unidirectional connectivity between neuronal networks grown in microfluidic chips [10,11,12,13,14,15,16,17]. Microelectrode arrays combined with microfluidic chips provide electrophysiological monitoring of spiking pattern generation and propagation within and between modular networks [20,21,22,23,24,25]. Such networks demonstrate a large number of spontaneous spiking patterns in contrast to uniform networks [26]. A high similarity between close-in-time spontaneous activity patterns was observed in modular networks, suggesting instantaneous memory-like phenomena [24]
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