A research team at Tohoku University used microfluidic devices to reveal how directional connections shape the complex dynamics of neuronal networks. They also developed mathematical models based on experimental data to predict how connectivity influences activity across space and time.

The results were published in Neural Networks on November 28, 2024.

Like a river current, directional connections in neuronal networks propagate signals in a downstream flow from one area to another. A microfluid device has tiny channels that can precisely direct the flow, which can help fabricate neurons that react more similarly to in-vivo models. By studying in-vitro neurons in a lab environment, the research team was able to efficiently and constructively explore whether one-way connections play other fundamental roles in shaping brain dynamics.

“The brain is difficult to understand, in part, because it is dynamic — it can learn to respond differently to the same stimuli over time based on a number of factors,” says lead author Nobuaki Monma.

The research team fabricated neuronal networks bearing modular connectivity (as observed in animals’ nervous systems) and embedded directional connections between modules using microchannels. The connections were embedded in a feedforward manner to minimize excessive excitatory reactions. Using calcium imaging to record spontaneous activity exhibited by the neuronal network, they found that networks incorporating directional connections exhibited more complex activity patterns compared to networks without directionality.

In addition, the researchers developed two mathematical models to clarify the underlying network mechanisms behind biological observations and to predict configurations that would yield greater dynamical complexity. The models determined that the interplay between modularity and connectivity fostered more complex activity patterns.

“The findings of this study are expected not only to deepen our fundamental understanding of neuronal networks in the brain, but also to find applications in fields such as medicine and machine learning,” proposes Associate Professor Hideaki Yamamoto.

This may also offer an in-vitro model for developing biologically plausible artificial neural networks. Further theoretical advancements would also contribute to modeling large-scale networks, which may provide insights to future connectome analysis of the brain. The more thoroughly we understand these neuronal networks, the more it could be used as a trusty tool to unlock the many mysteries of the brain.

Now, researchers at Princeton Engineering and the Indian Institute of Technology have harnessed artificial intelligence to take a key step toward slashing the time and cost of designing new wireless chips and discovering new functionalities to meet expanding demands for better wireless speed and performance. In an article published Dec. 30 in Nature Communications, the researchers describe their methodology, in which an AI creates complicated electromagnetic structures and associated circuits in microchips based on the design parameters. What used to take weeks of highly skilled work can now be accomplished in hours.

What is more, the AI behind the new system has produced strange new designs featuring unusual patterns of circuitry. Kaushik Sengupta, the lead researcher, said the designs were unintuitive and unlikely to be developed by a human mind. But they frequently offer marked improvements over even the best standard chips.

“We are coming up with structures that are complex and looks random shaped and when connected with circuits, they create previously unachievable performance. Humans cannot really understand them, but they can work better,” said Sengupta, a professor of electrical and computer engineering and co-director of NextG, Princeton’s industry partnership program to develop next-generation communications.

These circuits can be engineered towards more energy efficient operation or to make them operable across an enormous frequency range that is not currently possible. Furthermore, the method synthesizes inherently complex structures in minutes, while conventional algorithms may take weeks. In some cases, the new methodology can create structures that are impossible to synthesize with current techniques.

Uday Khankhoje, a co-author and associate professor of electrical engineering at IIT Madras, said the new technique not only delivers efficiency but promises to unlock new approaches to design challenges that have been beyond the capability of engineers.

“This work presents a compelling vision of the future,” he said. “AI powers not just the acceleration of time-consuming electromagnetic simulations, but also enables exploration into a hitherto unexplored design space and delivers stunning high-performance devices that run counter to the usual rules of thumb and human intuition.”

Wireless chips are a combination of standard electronic circuits like those in computer chips and electromagnetic structures including antennas, resonators, signal splitters, combiners and others. These combinations of elements are put together in every circuit block, carefully handcrafted and co-designed to operate optimally. This method is then scaled to other circuits, sub-systems and systems, making the design process extremely complex and time consuming, particularly for modern, high-performance chips behind applications like wireless communication, autonomous driving, radar and gesture recognition.

“Classical designs, carefully, put these circuits and electromagnetic elements together, piece by piece, so that the signal flows in the way we want it to flow in the chip. By changing those structures, we incorporate new properties,” Sengupta said. “Before, we had a finite way of doing this, but now the options are much larger.”

It can be hard to comprehend the vastness of a wireless chip’s design space. The circuitry in an advanced chip is so small, and the geometry so detailed, that the number of possible configurations for a chip exceeds the number of atoms in the universe, Sengupta said. There is no way for a person to understand that level of complexity, so human designers don’t try. They build chips from the bottom up, adding components as needed and adjusting the design as they build.

The AI approaches the challenge from a different perspective, Sengupta said. It views the chip as a single artifact. This can lead to strange, but effective arrangements. He said humans play a critical role in the AI system, in part because that AI can make faulty arrangements as well as efficient ones. It is possible for AI to hallucinate elements that don’t work, at least for now. This requires some level of human oversight.

“There are pitfalls that still require human designers to correct,” Sengupta said. “The point is not to replace human designers with tools. The point is to enhance productivity with new tools. The human mind is best utilized to create or invent new things, and the more mundane, utilitarian work can be offloaded to these tools.”

The researchers have used AI to discover and design complex electromagnetic structures that are-co-designed with circuits to create broadband amplifiers. Sengupta said future research will involve linking multiple structures and designing entire wireless chips with the AI system.

“Now that this has shown promise, there is a larger effort to think about more complicated systems and designs,” he said. “This is just the tip of the iceberg in terms of what the future holds for the field.”

In the elderly, however, excessive filtration cannot be detected due to age-related GFR decline. To accurately assess GFR, Osaka Metropolitan University researchers have come up with a new calculation method.

The group led by Dr. Akihiro Tsuda, a lecturer at the Graduate School of Medicine, assessed 180 kidney transplant donor candidates to define a new formula for determining the threshold value for hyperfiltration based on age and GFR values.

Among other findings, the conventional method of correcting for body surface area in obese patients was determined to be inaccurate as excessive filtration cannot be detected. The researchers suggest calculating GFR without the correction but by taking into account the decline in the filtration rate due to aging.

“Since hyperfiltration is a precursor to diabetic nephropathy, we hope that using this new formula will more accurately diagnose the condition, leading to early detection and treatment,” stated Dr. Tsuda.

The findings were published in Hypertension Research.