AUTHOR=Brofiga Martina , Tedesco Mariateresa , Bacchetti Francesca , Poggio Fabio , Burlando Bruno 

TITLE=In vitro clustered cortical networks reveal NMDA-dependent modulation of repetitive activation sequences

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 19 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2025.1650437

DOI=10.3389/fnins.2025.1650437

ISSN=1662-453X

ABSTRACT=The development of in vitro networks composed of distinct but interacting neuronal sub-populations (clusters) has advanced the study of emergent behaviors in neural networks as individual functional units. In a previous work, we developed an in vitro model of a network formed by four mutually interconnected clusters of rat embryonic cortical neurons cultured on multi-electrode arrays (MEA), where we observed recurring, spatially and temporally structured activation sequences. In the present study, we examined the effects of NMDAR blockade (MK-801) to modulate such temporal patterns. We found that MK-801 reduced the overall excitability of the network and disrupted the diversity of repeated activation patterns, while paradoxically increasing their temporal persistence. This led the network to transition from a dynamic regime characterized by frequent and flexible repetitions to one dominated by fewer, more stable and enduring activation motifs. Functional connectivity analysis further revealed a selective weakening of inter-cluster links alongside a strengthening of intra-cluster connections. This reorganization likely explains the observed reduction in activity propagation between clusters and the simultaneous emergence of more persistent activation sequences among clusters. Data suggest that clustered neural networks serve as semi-autonomous modules, capable of sustaining internal dynamics even under diminished excitatory drive. The stable repetition of activation patterns may reflect a functional “closure” within clusters, forming self-sustained loops that enable the reactivation of previously formed motifs. From a neuroengineering perspective, this model provides a versatile platform to explore how spatiotemporal neural dynamics underpin inter-network communication, information encoding, and complex cortical functions.