One year after unveiling a first-of-its-kind 'microwave brain' microchip capable of computing on ultrafast data and wireless signals, researchers from the Cornell Duffield College of Engineering have shown how the chip can encode information into its own language.
One year after unveiling a first-of-its-kind 'microwave brain' microchip capable of computing on ultrafast data and wireless signals, researchers from the Cornell Duffield College of Engineering have shown how the chip can encode information into its own language.
An EPFL team has created a new Large Language Model that is structured similarly to a human brain, allowing users more control and moving away from (black box) AI.
The circuitry underlying the remarkable efficiency of the brain is coming into sharper focus.
One year after unveiling a first-of-its-kind 'microwave brain' microchip capable of computing on ultrafast data and wireless signals, researchers from the Cornell Duffield College of Engineering have shown how the chip can encode information into its own language.
An EPFL team has created a new Large Language Model that is structured similarly to a human brain, allowing users more control and moving away from (black box) AI.
The circuitry underlying the remarkable efficiency of the brain is coming into sharper focus.
A team led by Ken Shepard unveils a high-precision platform that opens new possibilities for probing the brain's deep circuits.
One year after unveiling a first-of-its-kind 'microwave brain' microchip capable of computing on ultrafast data and wireless signals, researchers from the Cornell Duffield College of Engineering have shown how the chip can encode information into its own language.
An EPFL team has created a new Large Language Model that is structured similarly to a human brain, allowing users more control and moving away from (black box) AI.
The circuitry underlying the remarkable efficiency of the brain is coming into sharper focus.
A team led by Ken Shepard unveils a high-precision platform that opens new possibilities for probing the brain's deep circuits.
Fabricated as a single chip, the new implant is orders of magnitude faster and smaller than today's state-of-the-art brain-computer interfaces, offering an opportunity for more efficacious treatment of a number of neurological conditions.
5 minutes read
EPFL researchers have discovered key 'units' in large AI models that seem to be important for language, mirroring the brain's language system. When these specific units were turned off, the models got much worse at language tasks.
4 minutes read
Ismael Seáñez’s lab develops brain wave decoder that may help in spinal cord injury rehabilitation
3 minutes read
Brain–machine interfaces (BMIs) have enabled a handful of test participants who are unable to move or speak to communicate simply by thinking.
4 minutes read
Liu and team receive NSF EFRI grant for biocomputing research
Researchers from EPFL have developed a next-generation miniaturized brain-machine interface capable of direct brain-to-text communication on tiny silicon chips.
To strengthen the case for noninvasive BCIs, Carnegie Mellon researchers have demonstrated that through precision neuromodulation using focused ultrasound, the performance of a BCI could be improved for communication.
3 minutes read
In this episode, we discuss how a team from Carnegie Mellon University spearheading non-invasive brain computer interface solutions has had a significant breakthrough in improving their accuracy.
19 minutes read
Achieving a noteworthy milestone to advance noninvasive brain-controlled interfaces, researchers used AI technology to improve the decoding of human intention and control a continuously moving virtual object all by thinking about it, with unmatched performance.
3 minutes read
Organoid intelligence is a pioneering field where scientists use human brain cells to improve computer functions.
Barani Raman, professor of biomedical engineering in the McKelvey School of Engineering, is leading a multidisciplinary team to study how the locust brain transforms sensory input into behavior with a four-year, $4.3 million grant from the National Science Foundation’s Integrative Strategies for Understanding Neural and Cognitive Systems (NCS) program.
