AUTHOR=Weir Janelle Shari , Christiansen Nicholas , Sandvig Axel , Sandvig Ioanna TITLE=Selective inhibition of excitatory synaptic transmission alters the emergent bursting dynamics of in vitro neural networks JOURNAL=Frontiers in Neural Circuits VOLUME=17 YEAR=2023 URL=https://www.frontiersin.org/journals/neural-circuits/articles/10.3389/fncir.2023.1020487 DOI=10.3389/fncir.2023.1020487 ISSN=1662-5110 ABSTRACT=

Neurons in vitro connect to each other and form neural networks that display emergent electrophysiological activity. This activity begins as spontaneous uncorrelated firing in the early phase of development, and as functional excitatory and inhibitory synapses mature, the activity typically emerges as spontaneous network bursts. Network bursts are events of coordinated global activation among many neurons interspersed with periods of silencing and are important for synaptic plasticity, neural information processing, and network computation. While bursting is the consequence of balanced excitatory-inhibitory (E/I) interactions, the functional mechanisms underlying their evolution from physiological to potentially pathophysiological states, such as decreasing or increasing in synchrony, are still poorly understood. Synaptic activity, especially that related to maturity of E/I synaptic transmission, is known to strongly influence these processes. In this study, we used selective chemogenetic inhibition to target and disrupt excitatory synaptic transmission in in vitro neural networks to study functional response and recovery of spontaneous network bursts over time. We found that over time, inhibition resulted in increases in both network burstiness and synchrony. Our results indicate that the disruption in excitatory synaptic transmission during early network development likely affected inhibitory synaptic maturity which resulted in an overall decrease in network inhibition at later stages. These findings lend support to the importance of E/I balance in maintaining physiological bursting dynamics and, conceivably, information processing capacity in neural networks.