Nanowire networks are structures made up of interconnected nanowires. Nanowires are tiny wires made from materials such as silicon, germanium, or carbon nanotubes, with diameters on the order of a few nanometers. Nanowires can be grown in a variety of ways, including chemical vapor deposition, electrochemical deposition, and template-assisted growth. Once the nanowires are grown, they can be assembled into complex networks using techniques such as lithography, printing, and self-assembly. Nanowire networks have unique electronic and mechanical properties that make them attractive for a wide range of applications, including high-performance computing, energy storage, and sensing. Because of their small size, nanowire networks can be used to create devices with a high surface area-to-volume ratio, enabling efficient charge transfer and energy storage. Additionally, nanowire networks have the potential to be highly flexible and tunable, allowing them to be adapted to a variety of applications. Research in nanowire networks is still in its early stages, but it has the potential to revolutionize many fields of technology, including electronics, energy, and medicine. Nanowire networks and the brain share some similarities in their structure and function. Both nanowire networks and the brain have a complex network of interconnected elements. In the brain, neurons are interconnected by synapses, while in nanowire networks, nanowires are interconnected by junctions. These connections enable information transfer and processing. The structure of both nanowire networks and the brain is flexible and can be reconfigured to adapt to changing conditions. In the brain, this is known as neural plasticity, while in nanowire networks, it is known as electrical and mechanical tunability. Both nanowire networks and the brain process information through the transmission of electrical signals. In the brain, neurons transmit electrical signals to communicate information, while in nanowire networks, electrical signals travel through the nanowires. Moreover, both nanowire networks and the brain are highly efficient in their information processing capabilities. The brain is capable of complex tasks such as pattern recognition and decision-making, while nanowire networks have the potential to be used in high-performance computing applications.
The development of nanowire networks that mimic the functionality of the brain has the potential to revolutionize computing and artificial intelligence. One of the major challenges in developing such a system is the ability to mimic the complex interconnectivity and information processing capabilities of the brain. However, with advances in nanowire synthesis and assembly techniques, researchers are making progress towards building nanowire networks that can perform brain-like functions. One potential application of brain-inspired nanowire networks is in neuromorphic computing, which aims to build computers that mimic the architecture and function of the brain. Neuromorphic computing has the potential to be much more energy-efficient than traditional computing and could enable the development of advanced artificial intelligence systems. In addition to computing, brain-inspired nanowire networks could have applications in medical devices, such as brain-machine interfaces, where they could be used to monitor and control neural activity.
In new research published in the journal Science Advances, an international team led by Professor Zdenka Kuncic at the University of Sydney demonstrated nanowire networks can exhibit both short- and long-term memory like the human brain. The research team found higher-order cognitive function, which we normally associate with the human brain, can be emulated in non-biological hardware. The authors were building on previous research in which they showed how nanotechnology could be used to build a brain-inspired electrical device with neural network-like circuitry and synapse-like signaling. It paves the way towards replicating brain-like learning and memory in non-biological hardware systems and suggests that the underlying nature of brain-like intelligence may be physical.
Advances in nanowire networks could herald many real-world applications, such as improving robotics or sensor devices that need to make quick decisions in unpredictable environments. To test the capabilities of the nanowire network, the researchers gave it a test similar to a common memory task used in human psychology experiments, called the N-Back task. For a person, the N-Back task might involve remembering a specific picture of a cat from a series of feline images presented in a sequence. An N-Back score of 7, the average for people, indicates the person can recognize the same image that appeared seven steps back. When applied to the nanowire network, the researchers found it could ‘remember’ a desired endpoint in an electric circuit seven steps back, meaning a score of 7 in an N-Back test.
Neuroscientists think this is how the brain works, certain synaptic connections strengthen while others weaken, and that’s thought to be how we preferentially remember some things, how we learn and so on. The researchers said when the nanowire network is constantly reinforced, it reaches a point where that reinforcement is no longer needed because the information is consolidated into memory. Overall, the similarities between nanowire networks and the brain suggest that researchers can learn from the brain’s structure and function to develop more advanced nanowire networks that can perform complex tasks. In conclusion, the future of building nanowire networks that act like a brain is promising, with the potential to revolutionize many fields of technology. While there are still many challenges to overcome, researchers are making progress towards developing systems that can perform brain-like functions, and the potential applications are vast.
Loeffler A, Diaz-Alvarez A, Zhu R, Ganesh N, Shine JM, Nakayama T, Kuncic Z. Neuromorphic learning, working memory, and metaplasticity in nanowire networks. Sci Adv. 2023;9(16):eadg3289. doi: 10.1126/sciadv.adg3289.