Biological neural networks (BNNs) promise low-power consumption and massive parallelism, offering a plausible route toward truly bio-derived intelligence beyond conventional AI frameworks. However, their computational principles remain poorly understood, largely due to the lack of standardized experimental paradigms. Here, we fabricated a 256-channel in vitro microelectrode array (MEA) coated with Pt nanoparticles and PEDOT:PSS. The hybrid interface exhibits low impedance (15.33 ± 0.63 kΩ at 1 kHz), a high charge-storage capacity (87.30 ± 5.82 mC cm-2), 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.
Jiang et al. (Wed,) studied this question.