Synthetic neural data generation and neuroprosthetic devices are active areas of research, sparked by advances in neuroscience and robotics [22, 4, 2, 15]. These fields have significant implications for brain-computer interfaces, rehabilitation, and simulation of brain dynamics for downstream tasks or gaining new understanding of the underlying neural mechanisms. In this project, which we call "Project Jenkins," we explore such decoding and encoding of neural data from a macaque monkey named Jenkins. We used a publicly available dataset [5] containing neural firing patterns from Jenkins' motor and premotor cortical areas during a center-outreach task. Generating synthetic neural activity enables researchers to test and refine decoding models without requiring continuous access to live neural recordings [12, 16], while neuroprosthetic advancements [18, 20, 21, 9, 7, 3, 8, 17] rely on robust encoding techniques to translate brain signals into precise motor commands. Our aim was two-fold (Figure 1, 2): Decoding: Translate neural spiking data into predicted velocities for a robotic arm. Encoding: Generate synthetic neural activity corresponding to an intended robotic movement. With this paper, we publish the developed open-source tools for both synthetic neural data generation and neural decoding, enabling researchers to replicate our methods and build upon them. Our full codebase and additional resources, including demonstration videos, can be found on the project's website: https://www.808robots.com/

The new research was carried out at Stanford University. The preprint, published January 21, began drawing extra attention on Twitter and other social media because of the death this week of its co-lead author, Krishna Shenoy, from pancreatic cancer. Shenoy had devoted his career to improving the speed of communication through brain interfaces, carefully maintaining a list of records on his laboratory website. In 2019, another volunteer Shenoy worked with managed to use his thoughts to type at a rate of 18 words a minute, a record performance at the time, as we related in MIT Technology Review's special issue on computing. The brain-computer interfaces that Shenoy's team works with involve a small pad of sharp electrodes embedded in a person's motor cortex, the brain region most involved in movement.

Recently, a group in Spain, using an implant called the Utah array, which has 96 electrodes, found that that a blind woman could use such a system attached to her brain to make out letters. In its prior events, Neuralink has followed somewhat cautiously in the footsteps of other neuroscientists. For instance, in 2021, it showed a video of a monkey playing the video game Pong with its brain. However, a human with a brain implant had already played the game 15 years before. Instead of entirely new applications, what's actually important about Neuralink is that it has developed a sophisticated type of brain implant using thin wires studded with electrodes.

Are We on the Verge of Chatting With Whales? Christoph Droesser Hakai Magazine "An ambitious project is attempting to interpret sperm whale clicks with artificial intelligence, then talk back to them. The next step would be an interactive chatbot that tries to engage in a dialogue with free-living whales." Brain Implants Could Be the Next Computer Mouse Antonio Regalado MIT Technology Review "While other brain-interface researchers grabbed the limelight with more spectacular demonstrations, Shenoy's group has stayed focused on creating a practical interface that paralyzed patients can use for everyday computer interactions. Boston Dynamics Wants You to Know Its Spot Robot Has Moves Like Jagger I. Bonifacic Engadget "Is this what TikTok will look like when the robots take over?

Wikidata has been increasingly adopted by many communities for a wide variety of applications, which demand high-quality knowledge to deliver successful results. In this paper, we develop a framework to detect and analyze low-quality statements in Wikidata by shedding light on the current practices exercised by the community. We explore three indicators of data quality in Wikidata, based on: 1) community consensus on the currently recorded knowledge, assuming that statements that have been removed and not added back are implicitly agreed to be of low quality; 2) statements that have been deprecated; and 3) constraint violations in the data. We combine these indicators to detect low-quality statements, revealing challenges with duplicate entities, missing triples, violated type rules, and taxonomic distinctions. Our findings complement ongoing efforts by the Wikidata community to improve data quality, aiming to make it easier for users and editors to find and correct mistakes.

IMAGE: Stanford neuroscientists and engineers used neural implants to track decision making in the brain, in real time. In the course of deciding whether to keep reading this article, you may change your mind several times. While your final choice will be obvious to an observer - you'll continue to scroll and read, or you'll click on another article - any internal deliberations you had along the way will most likely be inscrutable to anyone but you. That clandestine hesitation is the focus of research, published Jan. 20 in Nature, by Stanford University researchers who study how cognitive deliberations are reflected in neural activity. These scientists and engineers developed a system that read and decoded the activity of monkeys' brain cells while the animals were asked to identify whether an animation of moving dots was shifting slightly left or right.

For the first time, researchers have deciphered the brain activity associated with trying to write letters by hand. Working with a participant with paralysis who has sensors implanted in his brain, the team used an algorithm to identify letters as he attempted to write them. Then, the system displayed the text on a screen -- in real time. The innovation could, with further development, let people with paralysis rapidly type without using their hands, says study coauthor Krishna Shenoy, a Howard Hughes Medical Institute Investigator at Stanford University who jointly supervised the work with Jaimie Henderson, a Stanford neurosurgeon. By attempting handwriting, the study participant typed 90 characters per minute -- more than double the previous record for typing with such a "brain-computer interface," Shenoy and his colleagues report in the journal Nature on May 12, 2021.