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Making computer chips act more like brain cells

The human brain is an amazing computing machine. Weighing only about three pounds, it can process information a thousand times faster than the fastest supercomputer, store a thousand times more information than a powerful laptop, and do it all using the power of a 20-watt light bulb.

The researchers are trying to replicate this success by using soft, flexible organic materials that can operate like biological neurons and might one day even interface with them. Eventually, soft “neuromorphic” computer chips could be implanted directly into the brain, allowing people to control an artificial arm or computer monitor just by thinking about it.

Like real neurons, but unlike conventional computer chips, these new devices can send and receive chemical and electrical signals. “Your brain works on chemicals, on neurotransmitters like dopamine and serotonin. Our materials can electrochemically interact with them,” says Alberto Salleo, a materials scientist at Stanford University who wrote about the potential of organic neuromorphic devices in 2021 Materials Research Annual Review.

Salleo and other researchers have created electronic devices using these soft organic materials that can act as transistors (which amplify and switch electrical signals) and memory cells (which store information) and other basic electronic components.

The work stems from a growing interest in neuromorphic computer circuits that mimic how human neural connections, or synapses, work. These circuits, whether made of silicon, metal, or organic materials, work less like those in digital computers and more like networks of neurons in the human brain.

Conventional digital computers work step by step, and their architecture creates a fundamental divide between computation and memory. This division means that the ones and zeros must be swapped between locations in the computer’s processor, creating a traffic jam for speed and energy use.

The brain does things differently. An individual neuron receives signals from many other neurons, and all of these signals together affect the electrical state of the receiving neuron. In effect, each neuron serves both as a computing device, integrating the value of all the signals it has received, and as a memory device: storing the value of all those combined signals as an infinitely variable analog value, instead of zero or zero. – one of digital computers.

Researchers have developed a number of different “memristive” devices that mimic this ability. When you pass electrical current through them, you change the electrical resistance. Like biological neurons, these devices calculate by adding the values ​​of all the currents to which they have been exposed. And they remember through the resulting value that their resistance takes.

A simple organic memristor, for example, might have two layers of electrically conducting materials. When a voltage is applied, the electric current drives positively charged ions from one layer to the other, changing how easily the second layer will conduct electricity the next time it is exposed to an electric current. (See diagram). “It’s a way of letting physics do the computation,” he says. Matthew Marinellaa computer engineer at Arizona State University in Tempe who researches neuromorphic computing.

The voltage applied at the gate (G), for example from a sensor, drives positive ions from one layer, called the electrolyte, to an adjacent layer, an organic polymer. This changes the resistance of the polymer to a current moving from source (S) to drain (D). The amount of resistance represents the value that is stored. Credit: Knowable; Source: “Organic electronics for neuromorphic computing”, by Yoeri van de Burgt et al., in NatureElectronics1. Posted on July 13, 2018

The technique also frees the computer from strictly binary values. “When you have classic computer memory, it’s either a zero or a one. We make a memory that can be any value between zero and one. So you can tune it analog,” says Salleo.

At the moment, most memristors and related devices are not based on organic materials, but use standard silicon chip technology. Some are even used commercially as a way to speed up AI programs. But organic components have the potential to get the job done faster using less energy, says Salleo. Better yet, they could be designed to integrate with your own brain. The materials are soft and flexible, and they also have electrochemical properties that allow them to interact with biological neurons.

For example, Francesca Santoro, an electrical engineer now at RWTH Aachen University in Germany, is developing a polymer device that takes information from real cells and “learn” from it. In his device, the cells are separated from the artificial neuron by a small gap, similar to the synapses that separate real neurons from each other. As the cells produce dopamine, a nerve-signaling chemical, the dopamine changes the electrical state of the artificial half of the device. The more dopamine the cells produce, the more the electrical state of the artificial neuron changes, just as can be seen with two biological neurons. (See diagram). “Our ultimate goal is really to design electronic devices that look like neurons and act like neurons,” says Santoro.

The biological neuron releases dopamine (red balls) at its junction with the artificial neuron. A solution in space gives dopamine a positive charge (gold balls), allowing it to flow through the device. Electrical resistance depends on how quickly dopamine is released and how much has accumulated in the artificial neuron. Credit: Knowable; Source: “A biohybrid synapse with neurotransmitter-mediated plasticity,” by Scott T. Keene et al., in materials from natureVol. 19. Released June 15, 2020

The approach could offer a better way to use brain activity to drive prosthesis either computer monitors Current systems use standard electronics, including electrodes that can only pick up broad patterns of electrical activity. And the equipment is bulky and requires external computers to run.

Flexible neuromorphic circuitry could improve this in at least two ways. They would be able to translate neural signals in a much more granular way, responding to signals from individual neurons. And the devices could also handle some of the necessary calculations themselves, says Salleo, which could save power and increase processing speed.

Low-level decentralized systems of this kind, with small neuromorphic computers that process information as it is received by local sensors, are a promising avenue for neuromorphic computing, Salleo and Santoro say. “The fact that they closely resemble the electrical functioning of neurons makes them ideal for physical and electrical coupling with neural tissue,” says Santoro, “and ultimately with the brain.”

This article originally appeared on well-known magazine, an independent reporting effort of Annual Reviews. Sign up for the Newsletter.


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