A brain-computer interface (BCI), also known as a brain-machine interface (BMI), is a direct communication pathway between the electrical activity of the brain and an external device, most commonly a computer or robotic limb. BCIs are frequently used to investigate, map, assist, augment, or repair human cognitive or sensory-motor functions. Based on how close electrodes are to brain tissue, BCI implementations range from non-invasive (EEG, MEG, EOG, MRI) to partially invasive (ECoG and endovascular) to invasive (microelectrode array).
Long has research sought to develop computers that use as little energy as our brains. For the first time, researchers at the University of Gothenburg succeeded in combining a memory function and a calculation function in the same component. The discovery paves the way for more efficient technologies ranging from smartphones to self-driving cars.
Computers have recently been able to tackle advanced cognitive tasks such as language and image recognition, as well as displaying superhuman chess skills, thanks in large part to artificial intelligence (AI). At the same time, the human brain continues to be unparalleled in its ability to perform tasks effectively and efficiently.
Researchers from Sweden’s University of Gothenburg and Japan’s Tohoku University successfully combined a memory function and a calculation function in the same component, marking the first time researchers have successfully linked oscillator networks and memristors.
This is a significant breakthrough because we demonstrate that it is possible to combine a memory function and a calculating function in the same component. These components function more like the brain’s energy-efficient neural networks, allowing them to be important building blocks in future, more brain-like computers.Johan Kerman
“These components work more like the brain’s energy-efficient neural networks, allowing them to become important building blocks in future, more brain-like computers,” said Johan Kerman of the University of Gothenburg. Kerman elaborated, “The more energy-efficient cognitive calculations can be performed, the more applications that can be developed. That is why our research has the potential to make a significant contribution to the field.”
“For decades, researchers have been working to develop new methods of performing calculations that mimic the brain’s energy-efficient processes. Cognitive tasks, such as image and voice recognition, necessitate a significant amount of computing power, and mobile applications, in particular, such as mobile phones, drones, and satellites, necessitate energy-efficient solutions “Johan Kerman, professor of applied spintronics at the University of Gothenburg, agrees.
Kerman led a study with a research team at Tohoko University that has now taken an important step toward achieving this goal. The researchers succeeded in linking the two main tools for advanced calculations, oscillator networks and memristors, for the first time in the study, which has now been published in the highly regarded journal Nature Materials.
Oscillators, according to Kerman, are oscillating circuits that can perform calculations and are analogous to human nerve cells. Memristors are programmable resistors with integrated memory and the ability to perform calculations. As a result, they are similar to memory cells. Integrating the two is a significant step forward for the researchers.
“This is a significant breakthrough because we demonstrate that it is possible to combine a memory function and a calculating function in the same component. These components function more like the brain’s energy-efficient neural networks, allowing them to be important building blocks in future, more brain-like computers.”
Enables energy-efficient technologies
According to Johan Kerman, the discovery will enable faster, easier-to-use, and less energy-consuming technologies in a variety of fields. He considers it a huge advantage that the research team was able to successfully produce the components in an extremely small footprint: hundreds of components fit into an area equivalent to a single bacterium. This is especially important in smaller applications like mobile phones.
“More energy-efficient calculations could lead to new mobile phone functionality. Digital assistants, such as Siri or Google, are an example. Because the calculations require too much energy for the small size of a phone, all processing is now done by servers. If the calculations could instead be performed locally, on the phone itself, they would be faster and easier to perform without the need to connect to servers.”
He cites self-driving cars and drones as other areas where more energy-efficient calculations could drive innovation. “The more energy-efficient cognitive calculations can be performed, the more applications that can be developed. That is why our research has the potential to make a significant contribution to the field.”
Concerning the field of study Neuromorphic computing is an AI-related field that attempts to mimic the neural networks of the brain. The research employs novel algorithmic approaches that mimic how the human brain integrates with its surroundings in order to deliver the capacity that is comparable to human cognition.
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