Brain cells learn faster than machine learning, research reveals - Hi Tech & Innovation - Machine learning & AI

Proposal of possible differential developments of OI and BI pathways, along with where on a series of spectrums each may exist. Credit: Cell Biomaterials (2025). DOI: 10.1016/j.celbio.2025.100156

"Our goal was to go beyond anecdotal demonstrations of biological learning and provide rigorous, quantitative evidence that living neural networks exhibit rapid and adaptive reorganization in response to stimuli—capabilities that remain out of reach for even the most advanced deep reinforcement learning systems," added Cortical Labs' Forough Habibollahi.

"While artificial agents often require millions of training steps to show improvement, these neural cultures adapt much faster, reorganizing their activity in response to feedback.

"By analyzing how their electrical signals evolved over time, we found clear patterns of learning and dynamic connectivity changes that mirror key principles of real brain function, demonstrating the potential of biological systems as fast, efficient learners."

Cortical Labs' Moein Khajehnejad added, "By converting high-dimensional spiking activity into interpretable, low-dimensional representations, we were able to uncover the internal plasticity and network reconfiguration patterns that accompany learning in biological neural cultures. These were not just statistical differences; they were real, functional reorganizations that paralleled improvements in task performance over time.

"What makes this study truly groundbreaking is that it's the first to establish a head-to-head benchmark between synthetic biological systems and deep RL under equivalent sampling constraints. When opportunities to learn are limited, a condition closer to how animals and humans actually learn, these biological systems not only adapt faster but do so more efficiently and robustly. That's an exciting and humbling result for the fields of AI and neuroscience alike."

Hideaki Yamamoto, Associate Professor at the Research Institute of Electrical Communication, Tohoku University, commented, "These synthetic biological systems will certainly provide a new approach to understanding the physical substrate of brain computation. Furthermore, they may open a new class of computing, especially in tasks that the brain excels at.

"The CL1 will be a strong platform for putting this vision into action. When I first met the team three years ago, they had just started discussing the idea of building their own MEA system. That they have developed the CL1 and brought it to commercialization in such a short time is deeply impressive." 

by Cortical Labs

edited by Lisa Lock, reviewed by Robert Egan 

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