Knowledge gaps for neuromorphic ionic computing.
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| Title: | Knowledge gaps for neuromorphic ionic computing. |
|---|---|
| Authors: | Aluru, Narayana R. (AUTHOR), Darling, Seth B. (AUTHOR), Elam, Jeffrey W. (AUTHOR), Gang, Oleg (AUTHOR), Salleo, Alberto (AUTHOR), Siwy, Zuzanna (AUTHOR), Talin, A. Alec (AUTHOR), Noy, Aleksandr (AUTHOR) |
| Source: | Science. 5/7/2026, Vol. 392 Issue 6798, p592-601. 10p. |
| Subjects: | Materials science, System integration, Artificial neural networks, Computer architecture, Energy conservation, Artificial intelligence |
| Abstract: | 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. Editor's summary: Neuromorphic ionic computing, which uses principles similar to a human brain, represents a groundbreaking direction in computational technology, promising substantially improved energy efficiency compared with traditional silicon-based platforms. In a Review, Aluru et al. highlight essential gaps in knowledge spanning multiple domains such as materials science, device design, system integration, chemical compatibility, and biocompatibility that must be addressed. The authors emphasize the critical role of interdisciplinary collaboration in realizing the full promise of this emerging field. By advancing these areas, neuromorphic ionic systems could provide new possibilities for energy-efficient computing, with applications ranging from artificial intelligence to robotics and beyond. —Yury Suleymanov BACKGROUND: Neuromorphic computing, inspired by the human brain's ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. We propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES: Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and "multichromatic" computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK: Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation. Neuromorphic computing platforms.: Biological brains have compex 3D networks of neurons and synapses to perform complex computations with utmost efficiency. Current large arrays of solid-state devices do not capture the complexity and diversity of the brain. Neuromorphic ionic devices have the potential to achieve next-level connectivity, functional diversity, and energy efficiency on the road to sustainable computing. [ABSTRACT FROM AUTHOR] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| Abstract: | 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. Editor's summary: Neuromorphic ionic computing, which uses principles similar to a human brain, represents a groundbreaking direction in computational technology, promising substantially improved energy efficiency compared with traditional silicon-based platforms. In a Review, Aluru et al. highlight essential gaps in knowledge spanning multiple domains such as materials science, device design, system integration, chemical compatibility, and biocompatibility that must be addressed. The authors emphasize the critical role of interdisciplinary collaboration in realizing the full promise of this emerging field. By advancing these areas, neuromorphic ionic systems could provide new possibilities for energy-efficient computing, with applications ranging from artificial intelligence to robotics and beyond. —Yury Suleymanov BACKGROUND: Neuromorphic computing, inspired by the human brain's ability to process information efficiently, represents a transformative approach to computation. In this Review, we explore the emerging field of neuromorphic ionic computing, which leverages ionic conduction and coupling to mimic neural processes, and identify critical knowledge gaps that must be addressed to realize its full potential. A central theme of the discussion is energy efficiency, a challenge that is both a limitation and an opportunity for this technology. Although complementary metal-oxide semiconductor (CMOS)–based neuromorphic technologies have made strides in scaling to billions of neurons and are increasingly applied in artificial intelligence and numerical computing, they remain orders of magnitude behind the human brain in terms of connectivity and energy efficiency. Neuromorphic ionic computing promises to overcome these limitations by leveraging the distinct architectural and operational principles of the brain. Our brains achieve this energy efficiency by combining several key features: using the same network elements to store and process information; using an incredibly complex and massively interconnected three-dimensional (3D) network of locally active elements that enables sparsity, robustness in the presence of noise, adaptation, and life-long learning; computing at comparatively low voltage and frequency; and last, taking advantage of a plethora of ions and small molecules as information carriers. We propose that ionic computing systems can take advantage of similar features to achieve substantial gains in energy efficiency. ADVANCES: Since the first reports of neuromorphic ionic behavior in nanofluidic channels, we have witnessed an explosion of reports that used ionic devices to produce synaptomimetic behaviors. However, achieving the goals of ionic computing requires not only implementation of much more sophisticated device functionality but also overcoming fundamental barriers in materials science, device architecture, and system integration. Current ionic devices, even those incorporating state-of-the-art materials, still suffer from limited functionality and stability, which restrict their performance and increase energy demands. Developing new materials with enhanced ionic properties is essential to overcome these limitations. Similarly, the design of neuromorphic devices must evolve to leverage the particular advantages of ionic processes. Existing architectures often follow a single-information-carrier logic of conventional electronics or are constructed of mesoscale fluidics, failing to capitalize on the energy-efficient mechanisms inherent to ionic systems or implement the multiple-information-carrier paradigm. Current neuromorphic chips focus on large-scale networks of analog memory elements based on mechanisms such as charge trap (flash), filamentary, phase change, or spin, which are built on top of a network of artificial CMOS neurons. Although such prototype networks have achieved impressive performance, it is difficult to envision how they can implement the key features such as massive connectivity, sophisticated plasticity, adaptability, sparsity, and "multichromatic" computing. Although small-scale devices have demonstrated promising results, integrating them, maintaining energy efficiency, and implementing temperature control as systems grow in complexity and size to computationally relevant scale remain major hurdles. Furthermore, interfacing neuromorphic ionic devices with existing computing technologies presents technical and conceptual challenges that will require innovative approaches that combine insights from neuroscience, materials science, and engineering. OUTLOOK: Despite these challenges, the potential impact of neuromorphic ionic computing is profound with potential applications ranging from artificial intelligence to robotics and beyond. We also argue that neuromorphic ionic computing systems should not, at least in the beginning, compete with CMOS technologies but rather should focus on applications that require extreme energy efficiency with chemical and/or biological compatibility, such as biomedical applications (for example, brain-computer interfaces), environmental monitoring, and agricultural and food applications. Ultimately, this Review highlights the crucial role of interdisciplinary collaboration in advancing the field. Neuromorphic ionic computing is not merely a technological innovation; it represents a substantial step toward sustainable computation, aligning with the growing demand for energy-conscious solutions in a world that is increasingly reliant on data and computation. Neuromorphic computing platforms.: Biological brains have compex 3D networks of neurons and synapses to perform complex computations with utmost efficiency. Current large arrays of solid-state devices do not capture the complexity and diversity of the brain. Neuromorphic ionic devices have the potential to achieve next-level connectivity, functional diversity, and energy efficiency on the road to sustainable computing. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 00368075 |
| DOI: | 10.1126/science.aea2097 |
