Knowledge gaps for neuromorphic ionic computing
Summary
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, mate
Content
# Knowledge gaps for neuromorphic ionic computing
*Published: 2026 May 7*
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.
DOI: 10.1126/science.aea2097