Scientists at the University of Illinois Urbana Champaign have discovered evidence that could change the way researchers think about both the brain and artificial intelligence. Their findings suggest that decision-making begins much earlier in the brain than traditional theories propose, offering new ideas for designing future AI systems that are more capable and much more energy efficient.
Led by electrical and computer engineering professor Yurii Vlasov of the Grainger College of Engineering, the research was published in Proceedings of the National Academy of Sciences (PNAS). The study points to an unexpected role of the brain’s early sensory regions in decision-making, challenging the long-accepted view that decisions emerge only after information passes through a strict hierarchy of brain regions.
Rethink how the brain makes decisions
The human brain is widely considered the most complex structure in the known universe. Scientists still don’t fully understand how it works, which is why the National Academy of Engineering identified reverse engineering the brain in 2008 as one of the 14 great engineering challenges of the 21st century.
For decades, many artificial intelligence systems, including convolutional neural networks, have been inspired by the idea that the brain processes information in a unidirectional sequence. According to this traditional model, sensory information travels upward through increasingly complex brain regions until it reaches the frontal cortex, where decisions are made.
Vlasov and other researchers have increasingly questioned whether that picture is complete.
Instead, they are exploring a model based on natural intelligence, which has been refined through evolution over hundreds of millions of years. In this framework, the brain does not rely solely on a step-by-step flow of information. Decision making also depends on interconnected feedback loops that allow information to move in both directions between brain regions.
Because biological intelligence performs remarkably complex tasks and uses much less energy than current AI systems, understanding this architecture could help guide the development of future AI.
“We want to learn from a billion years of evolution,” Vlasov said. “How is that biological intelligence organized architecturally? Can we learn from the architectural side of the brain and emulate it to make AI more effective, less energy-consuming, and more intelligent than it currently is? At the decision-making level, that’s where current AI is lacking.”
Early brain regions show decision-making activity
To investigate how these processes work, the research team focused on the brain’s early stages of sensation and perception.
The scientists recorded neural activity in mice as they navigated a virtual reality corridor and made perceptual decisions. They found evidence of activity related to decision-making in the primary somatosensory cortex (S1), one of the first sensory processing areas of the brain.
Rather than simply transmitting information, S1 appeared to be influenced by higher brain regions through feedback loops. This top-down regulation suggests that decision-making involves continuous communication across multiple brain areas rather than a simple one-way flow of information.
“The neural code of the brain is still largely an unknown language,” Vlasov said. “But this systems-level understanding can be seen as potentially impacting how more efficient artificial neural networks can be built, how the next generation of AI can be thought of. Maybe with these analogies we learn from real brains, we can improve AI even further.”
What the findings could mean for future AI
The researchers emphasize that the study does not provide a blueprint for building better artificial intelligence. Instead, it offers new insights into how the brain organizes decision-making that could eventually inspire future AI architectures.
Next, Vlasov and his team plan to investigate the timing of these brain signals in more detail. They also aim to develop new technologies to measure neural activity to better understand how feedback loops arise and coordinate different levels of brain processing.
“By looking at the rapid temporal dynamics of neural activity, perhaps we can better understand how these feedback loops mediate decision making,” Vlasov said. “Maybe that’s the approach that potentially uncovers these currently unknown mechanisms: how these feedback loops are dynamically organized and how they form and shape different levels of processing. Maybe that can be implemented in new architectures for AI.”