'Understanding the brain is directly tied to designing future computing'

Researchers have developed a technology that uses semiconductor chips to measure electrical signals in the human brain on a large scale, with potential applications in neuromorphic computing — a next-generation semiconductor field that designs AI functions by mimicking the structure of the brain.

The Chey Institute for Advanced Studies, chaired by Chey Tae-won of SK Group, hosted a special lecture by Ham Donhee, a chair professor at Harvard University's John A. Paulson School of Engineering and Applied Sciences, on May 28 at the Korea Foundation for Advanced Studies building in Gangnam-gu, Seoul. Ham graduated top of his class from Seoul National University's physics department and joined Harvard's faculty in 2002 at age 28, making him the youngest Korean professor ever appointed there.

At the lecture, Ham introduced a system his research team spent more than a decade developing: the iMEA, or Intracellular Microelectrode Array. The iMEA integrates roughly 4,000 electrodes onto a single chip, enabling large-scale measurement of electrical signals inside living neurons.

"This significantly extends the limits of existing technology in terms of both the scale of synaptic connectivity and the precision of individual neuron data," Ham said. "Our goal is a new approach that can analyze large-scale neural networks while preserving cell-level information."

Ham said he expects the findings to serve as a foundational technology for next-generation neuromorphic chip design. Neuromorphic semiconductors replicate the structure of the human brain's neural network in hardware to enable AI capabilities such as deep learning.

"Current computers separate memory and processing, but in the brain, memory and computation happen within a single network," Ham said. "Understanding how the brain works goes beyond neuroscience — it is directly tied to designing the computing paradigms of the future."