Functional Cell-Type Identification in Neuronal Networks Using High-Density Microelectrode Arrays
The reliable identification of neuronal cell types - in particular, the distinction of excitatory (E) and inhibitory (I) neurons on the basis of extracellular recordings without post-hoc immunostaining or genetic labeling - remains a key challenge in neural-circuit analysis. High-density microelectrode arrays (HD-MEAs) have emerged as a powerful tool to address this issue, enabling simultaneous single-cell and network-level electrophysiology. Here, we present two complementary strategies for establishing cell-type ground truth based on HD-MEA recordings: (i) chemogenetic interneuron activation to label putative inhibitory neurons according to their functional response, and (ii) controlled mixing of excitatory and inhibitory hiPSC-derived populations at defined ratios. A classifier combining action potential waveform morphology and autocorrelogram-based discharge dynamics achieves robust cell-type discrimination in in vitro recordings of rat primary cortical cultures and hiPSC-derived networks, as well as in in vivo recordings of rat and mouse - i.e., across several species, recording modalities, and preparation types. Applied to unlabeled data, the classifier reveals cell-type-specific network dynamics during bursts, including an inhibition activity signature preceding burst onsets. Leveraging the HD-MEA spatiotemporal resolution, label-free electrophysiological footprint reconstruction enables a morphological characterization of putative E and I neurons without post-hoc staining. The classification pipeline represents a scalable framework for functional cell-type phenotyping with broad relevance for precision neural-circuit analysis and disease modeling.