In Vitro Biological Neuronal Networks Achieve Low-Power Consumption and High-Speed Communication through Predictable Stimulation

), and excellent biocompatibility. This configuration facilitates stable, long-term electrophysiological recording and electrical stimulation of cultured hippocampal neuronal networks. Using this platform, we systematically evaluated two electrical-stimulation paradigms: predictable electrical stimulation (PES), delivered as temporally regular, patterned pulses, and unpredictable electrical stimulation (UES), delivered as pseudorandom pulse sequences. PES-trained networks consumed less metabolic energy to generate action potentials and achieved a 1.79 ± 0.32-fold increase in network communication velocity throughput relative to unstimulated controls. In contrast, UES induced highly variable firing and synaptic reconfiguration, yielding greater network entropy but no net gain in communication speed. These findings suggest that temporal predictability is a key driver of energy-efficient, high-bandwidth computation in BNNs, whereas stochastic inputs primarily promote structural plasticity. The PtNPs/PEDOT:PSS-coated MEA and stimulation paradigm presented here provide a scalable testbed for dissecting BNN computing rules and training low-power, high-speed biohybrid processors.