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Uncovering the relationship between structure (connectivity) and function (neuronal activity) is a fundamental question across many areas of biology. However, investigating this directly in animal brains is challenging because of the immense complexity of their neural connections and the invasive surgeries that are typically needed. Lab-grown neurons with artificially controlled connections may become a useful alternative to animal testing, particularly as we learn how to accurately characterize their behavior.

A research team at Tohoku University has 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 in Neural Networks.

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," says 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 into future connectome analysis of the brain. The more thoroughly we understand these neuronal networks, the more they could be used as trusty tools to unlock the many mysteries of the brain.