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Organic transistors bring us closer to brain-mimicking AI

It has the ability to learn, and is equipped with both short-term and long-term memory.

Tibi PuiubyTibi Puiu
February 8, 2019
in Neurology, News
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Simone Fabiano and Jennifer Gerasimov. Credit: Thor Balkhed.

A new type of transistor based on organic materials might one-day become the backbone of computing technology that mimics the human brain. This kind of hardware is able to act like both short-term and long-term memory. It can also be modulated to create connections where there were none previously, which is similar to how neurons make synapses.

Your typical run-off-the-mill transistor acts as a sort of valve, allowing electrical current from an input to pass. In the process, it can be switched on and off. It can also be amplified or dampened.

The new organic transistor developed by researchers at Linkoping University in Sweden can create a new connection between an input and output through a channel made out of a monomer called ETE-S. This organic material is water-soluble and forms long polymer chains with an intermediate level of doping.

This electropolymerized conducting polymer can be formed, grown or shrunk, or completely removed during operation. When ions are injected through the channel, the electrochemical transistor can amplify or switch electron signals, which can be manipulated within a range that spans several orders of magnitude, as reported in the journal Science Advances. 

“We have shown that we can induce both short-term and permanent changes to how the transistor processes information, which is vital if one wants to mimic the ways that brain cells communicate with each other,” Jennifer Gerasimov, a postdoc in organic nanoelectronics at Linkoping University in Sweden and one of the authors of the article, said in a statement.

That’s similar to how neurons form new connections where there have been no prior connections. Today’s artificial neural networks use machine learning algorithms to recognize patterns through supervised or unsupervised learning. This brain-mimicking architecture requires prefabricated circuitry made of a huge number of nodes to simulate a single synapse. That’s a lot of computing power, which requires a lot of energy. In contrast, the human brain controls 100 billion neurons while running on 15 Watts of power — that’s a fraction of what a typical light bulb needs to function.

 “Our organic electrochemical transistor can therefore carry out the work of thousands of normal transistors with an energy consumption that approaches the energy consumed when a human brain transmits signals between two cells,” said Simone Fabiano, principal investigator in organic nanoelectronics at the Laboratory of Organic Electronics, Campus Norrköping.

The organic transistor looks like a promising prospect for neuromorphic computing — an umbrella term for endeavors concerned with mimicking the human brain, drawing upon physics, mathematics, biology, neuroscience, and more. According to a recent review, the neuromorphic computing market could grow to $6.48 bln. by 2024.

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Tags: AImachine learningtransistor

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Tibi Puiu

Tibi Puiu

Tibi is a science journalist and co-founder of ZME Science. He writes mainly about emerging tech, physics, climate, and space. In his spare time, Tibi likes to make weird music on his computer and groom felines. He has a B.Sc in mechanical engineering and an M.Sc in renewable energy systems.

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