An AI image recognition algorithm can predict whether a mouse is moving or not based on brain functional imaging data. The researchers from Kobe University have also developed a method to identify which input data is relevant, shining light into the AI black box with the potential to contribute to brain-machine interface technology.
For the production of brain-machine interfaces, it is necessary to understand how brain signals and affected actions relate to each other. This is called “neural decoding,” and most research in this field is done on the brain cells’ electrical activity, which is measured by electrodes implanted into the brain. On the other hand, functional imaging technologies, such as DOI: 10.1371/journal.pcbi.1011074
This research was funded by the Japan Society for the Promotion of Science (grants JP16H06316, JP23H04233, JP23KK0132, JP19K16886, JP23K14673 and JP23H04138), the Japan Agency for Medical Research and Development (grant JP21wm0425011), the Japan Science and Technology Agency (grants JPMJMS2299 and JPMJMS229B), the National Center of Neurology and Psychiatry (grant 30-9), and the Takeda Science Foundation. It was conducted in collaboration with researchers from the ATR Neural Information Analysis Laboratories.