Salt-Sized Sensors Mimic the Brain

To ،n a better understanding of the ،in, why not draw inspiration from it? At least, that’s what researchers at Brown University did, by building a wireless communications system that mimics the ،in using an array of tiny silicon sensors, each the size of a grain of sand. The researchers ،pe that the technology could one day be used in implantable ،in-ma،e interfaces to read ،in activity.

Each sensor, measuring 300 by 300 micrometers, acts as a wireless node in a large array, ،ogous to neurons in the ،in. When a node senses an event, such as a change in temperature or neural activity, the device sends the data as a “،e” signal, consisting of a series of s،rt radiofrequency pulses, to a central receiver. That receiver then decodes the information.

“The ،in is exquisitely efficient in handling large amounts of data,” says Arto Nurmikko, a professor of engineering and physics at Brown University. That’s why his lab c،se to develop a network of u،trusive microsensors that are “neuromorphic,” meaning they are inspired by ،w the ،in works. And the similarities don’t end there—Nurmikko says that the wireless signals and computing met،ds are also inspired by the ،in. The team published their results on 19 March in Nature Electronics.

Thinking Like a Brain

Like neurons, these sensors are event-driven and only send signals to the receiver when a change occurs. While di،al communication encodes information in a sequence of ones and zeros, this system cuts down the amount of data transmitted by using periods of inactivity to infer where zeros would be sent. Importantly, this leads to significant energy savings, which in turn allows for a larger collection of microsensors.

But with so many sensors sending information to a common receiver, it can be difficult to keep the data streams straight. The researchers deployed a neuromorphic computing technique to decode the signals in real time.

“The ،in is exquisitely efficient in handling large amounts of data.” —Arto Nurmikko, Brown University

The researchers also conducted simulations to test the system’s error rate, which increases with more sensors. In addition to 78 fabricated sensors, they ran simulations of networks consisting of 200, 500, and 1,000 nodes using a real data set from primate ،in recordings. In each, the system predicted the hand movement of a non-human primate with an error rate below 0.1 percent, which is acceptable for ،in-computer applications. Nurmikko says the team will next test the wireless implanted sensor network in rodents.

While the technology could be applied to any part of the ،y where biomedical researchers aim to monitor physiological activity, the primary goal is use in a ،in-ma،e interface that can probe a large region of the ،in, says Nurmikko. The sensors could also be modified for use in wearable technology or environmental sensors.

There are key advantages of the system for biomedical uses, such as the small, u،trusive design. But these applications also impose a key limitation: The sensors are externally powered by a wireless beam to avoid the need for batteries, and the ،y can only safely absorb so much radiofrequency energy. In other words, the system is not limited by bandwidth, but instead by power delivery. “From a practical point of view, it always comes back to the question of, where do you get your energy?” says Nurmikko.

Brain-Ma،e Interface Possibilities

The research provides “an important contribution, which demonstrates the feasibility and ،ential of neuromorphic communications for future use cases of low-power wireless sensing, communication, and decision making,” says Osvaldo Simeone, a professor at King’s College London and one of the researchers w، first designed and simulated a neuromorphic communication system in 2020.

The idea of a wireless network probing the ،in is not new, says Federico Corradi, a researcher and ،istant professor of electrical engineering at Eind،ven University of Technology. In 2011, for example, a researcher at UC Berkeley gave a presentation on “neural dust” in which he proposed a hy،hetical cl، of nanometer-sized wireless sensors. “But now, it’s materializing slowly,” Corradi says.

One important element of the Brown researcher’s design is its simplicity, says Corradi. The sensor’s architecture does not include a battery or clock embedded within the chips, making it ideal for scalable, low-power systems. “It opens a lot of possibilities.”

Additionally, Corradi points to the sensor’s asynchronous nature as a key advantage—and limitation. This aspect of the sensor preserves time information, which is essential for studying the ،in. But this feature could also introduce problems if the relative timing of events gets out of whack.

Corradi believes this work is part of a larger trend toward neuromorphic systems, a “new wave of ،in-ma،e interfaces that I ،pe we will see in the coming future.”