Summary: The arrangement of neurons and the recurring patterns they form help explain neural behavior and function, with potential applications for building more adaptable, intelligent robots.
Source: Newcastle University
Researchers say the spatial patterns formed by neurons reveal principles of brain function and could guide advances in artificial intelligence and robotics.
In new research published in PLoS Computational Biology, an international team from Newcastle University, the University of Zurich, ETH Zurich and the California Institute of Technology demonstrate that the apparent complexity of neuronal networks can be understood through a small set of developmental rules. By modelling neurons in the visual cortex—the brain region responsible for processing sight—the team found that many of the seemingly random arrangements actually follow predictable patterns.
The study shows that these recurring motifs are not just structural curiosities: they carry information about how neurons connect and communicate. Understanding these motifs helps reveal how neural circuits organise themselves during development and how they process visual inputs.
Dr Roman Bauer, co‑author of the study and a Research Fellow in the School of Computing at Newcastle University, explains how this perspective simplifies the problem of deciphering neural wiring:
“At first glance, the brain’s network of neurons looks tangled and impenetrable. But our findings show that particular neurons tend to form specific patterns that follow simple developmental rules. Once you recognise those patterns, you can begin to predict how those neurons behave and how they connect to the rest of the network.”
“If we can spot these patterns in the brain, we can use them to predict how those particular neurons are behaving,” Dr Bauer said.
The team concentrated on the connections between the thalamus—a deep brain structure that relays sensory information—and the cortical regions that interpret visual signals. By simulating developmental processes and observing the resulting network motifs, they identified stable patterns that recur across different simulations and parameter settings. These motifs provide insight into how neurons wire themselves to achieve reliable sensory processing.
One practical implication of the work is its relevance to artificial intelligence and robotics. Biological vision systems are remarkably robust: when the orientation, size or lighting of an object changes, the brain still recognises it as the same object. Many current AI systems struggle with this kind of invariance. The researchers suggest that translating a small set of biologically inspired patterns into computational principles could improve machine vision and make robotic perception more flexible and reliable.
Dr Bauer highlighted both technological and clinical potential:
“If we can distil the complexity of brain networks into a handful of meaningful patterns, engineers could use those principles to build AI that better mimics human perception. Equally important, recognising what a healthy network looks like gives us a baseline for detecting abnormalities, which could inform new diagnostic tools and treatments for neurological conditions.”
Source:
Newcastle University
Media Contacts:
Louella Houldcroft – Newcastle University
Image Source:
The image is in the public domain.
Original Research: The study is published in PLOS Computational Biology, where the authors describe their computational models and the developmental rules that give rise to the observed neuronal patterns.