Knowledge gaps for neuromorphic ionic computing

Neuromorphic ionic computing is inspired by the brain’s use of ions for ultralow-energy computation—its massive parallelism, adaptability, and learning capabilities. This emerging paradigm can overcome limitations of conventional silicon-based computing by enabling colocated memory and processing, multicarrier information streams, and massive three-dimensional connectivity. However, substantial knowledge gaps remain in understanding and engineering ionic transport, energy dissipation, materials design, and scalable device architectures. This Review explores these critical challenges across seven key domains, highlighting the need for new theoretical approaches, materials, device concepts, and fabrication strategies. We argue that advancing ionic neuromorphic systems requires an interdisciplinary approach, integrating insights from biology and neuroscience, nanofluidics, materials science, and systems engineering to enable a new class of energy-efficient, robust, and reconfigurable computing technologies.