#Neuralnetworks

A collaboration between the University of Göttingen and the Max Planck Institute for Dynamics and Self-Organization has unveiled “infomorphic neurons” that learn autonomously by mimicking brain-like processes. Published in the Proceedings of the National Academy of Sciences, this work shifts away from traditional supervised neural networks toward self-organizing artificial units. The neurons can determine which inputs matter for learning, reducing the need for constant external guidance.

The human brain operates through decentralized, energy-efficient networks. Biological neurons learn by responding to neighboring cells rather than following rigid, pre-set pathways. Infomorphic neurons imitate this adaptability, selecting learning goals and rules with minimal external control. With self-organization and specialization, these networks promise more robust problem solving in real-world tasks.

A new study from researchers at the University of Delaware combines artificial intelligence with Magnetic Resonance Elastography (MRE) to map brain stiffness. The approach aims to improve predictions of healthy brain age and help detect early signs of neurodegenerative diseases such as Alzheimer’s. Led by Curtis Johnson and Austin Brockmeier, the work shows how stiffness measurements, alongside brain volume, can yield the most accurate biologically derived age estimates yet.

In Thailand’s context, an aging population and rising neurological concerns make this research highly relevant. Understanding how brain stiffness relates to cognitive decline could support earlier diagnosis and better management of conditions within Thai healthcare, where modernization and traditional practices often coexist. The method uses gentle vibrations during MRI scanning to produce a stiffness map, offering new insights into how different brain regions respond to aging and disease.