New Transistor Inspired by Human Brain Functions like a Human Brain

Brain Like Computing

Researchers have developed a groundbreaking synaptic transistor that imitates the human brain’s integrated processing and memory capabilities. This device functions at room temperature, is energy-efficient, and can carry out complex cognitive tasks such as associative learning, making it a significant advancement in the field of artificial intelligence. Credit: Xiaodong Yan/Northwestern University

A transistor carries out energy-efficient associative learning at room temperature.

Utilizing the complex workings of the human brain, a group of researchers from Northwestern University, Boston College, and the Massachusetts Institute of Technology (MIT) has produced an innovative synaptic transistor.

This state-of-the-art device not only processes but also stores information, reflecting the multifunctional nature of the human brain. Recent experiments by the team have proved that this transistor surpasses simple machine-learning tasks to categorize data and is capable of performing associative learning.

Although previous studies have utilized similar strategies to develop brain-like computing devices, those transistors cannot function outside cryogenic temperatures. In contrast, the new device is stable at room temperatures. It also operates at fast speeds, consumes very little energy, and retains stored information even when power is removed, making it ideal for real-world applications.

The study was recently published in the journal Nature.

Imitating the Brain’s Efficiency

“The brain has a fundamentally different architecture than a digital computer,” said Northwestern’s Mark C. Hersam, who co-led the research. “In a digital computer, data move back and forth between a microprocessor and memory, which consumes a lot of energy and creates a bottleneck when attempting to perform multiple tasks at the same time. On the other hand, in the brain, memory and information processing are co-located and fully integrated, resulting in orders of magnitude higher energy efficiency. Our synaptic transistor similarly achieves concurrent memory and information processing functionality to more faithfully mimic the brain.”

Hersam is the Walter P. Murphy Professor of Materials Science and Engineering at Northwestern’s McCormick School of Engineering. He also is chair of the department of materials science and engineering, director of the Materials Research Science and Engineering Center, and member of the International Institute for Nanotechnology. Hersam co-led the research with Qiong Ma of Boston College and Pablo Jarillo-Herrero of

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