The research, presented at the Society for Neuroscience's annual meeting in San Diego this week, marked the latest advance by scientists who are trying to perfect the link between brain and machine.
One center, led by Andersen, will focus on stimulation, intention and perception — advancing Caltech's work on interfaces between brains and machines to help those who have lost sensation from paralysis or brain diseases.
The ties between brain science and machine learning are strikingly close in the study of vision: computer vision, i.e., the use of computers and machine learning to analyze and interpret images, has built upon, but also fostered our understanding of the brain visual system.
The papers are grouped thematically on cognitive and computational foundations of brain science, human information processing systems, brain big data analysis, curation and management, informatics paradigms for brain and mental health research, brain-machine intelligence and brain-inspired computing.
The function of chronic brain machine interfaces depends on stable electrical contact between neurons and electrodes.
Our own Tom Simonite reports that some researchers have shown that machine learning algorithms can spot links between brain signals and a person's typing, making it far easier to guess their password.
Neuroplasticity is key to the operation of brain machine interfaces (BMIs)—a direct communication pathway between the brain and a man-made computing device.
Brain machine interfaces (BMIs) attempt to build direct links between brains and artificial devices, such as computer cursors and robotic arms [ 1– 5].
Looking at 2020 and beyond, readers imagined a future with cures for intractable diseases, direct links between brain and computer, automated everything, contact with alien life forms, sentient machines and no language barriers.
In this classic work, one of the greatest mathematicians of the twentieth century explores the analogies between computing machines and the living human brain.
