DIRECTED RECONFIGURATION OF NEURONAL NETWORK CONNECTIVITY USING SCALABLE ELECTRICAL STIMULATION

Activity-dependent modification of synaptic connectivity is central to learning and computation in neuronal networks, yet experimentally steering such plasticity in a controlled and scalable manner remains a major challenge. In particular, applying Hebbian-like rules across networks and reliably validating the resulting changes has been limited by invasiveness of the methods, low throughput, or insufficient readout capabilities. In this work, we present a combined experimental and analytical framework for large-scale, noninvasive manipulation of neuronal circuits based on high-density microelectrode array (HD-MEA) technology. By combining functional connectivity inference with temporally precise, spatially patterned electrical stimulation, our approach enables targeted modulation of spike timing relationships across many connected neuron pairs in parallel.

To assess stimulation-induced plasticity in a scalable and model-free manner, we introduce Conditional Activity Metrics (CAM), which capture systematic changes in spike timing and firing statistics between neuronal pairs following intervention. In simulations, CAM reliably reflected underlying synaptic weight modifications. Applying the framework to rat cortical cultures, stem-cell-derived mouse cerebral organoids, and acute mouse cortical slices, we achieved consistent strengthening or weakening of functional connections in approximately 40% of the 279 tested neuron pairs. For selected pairs monitored over longer timescales, the induced effects remained stable for up to 90 min. Direct electrophysiological validation using combined HD-MEA and patch-clamp recordings confirmed corresponding synaptic changes.

Together, these results establish an all-electrical, noninvasive, high-throughput platform for directing and verifying plasticity at the network level, opening new possibilities for circuit-level neuroscience, neuroengineering, and biohybrid computing systems.