When someone loses the ability to speak because of a neurological condition like ALS, the impact goes far beyond words. Now, thanks to a team at the University of California, Davis, there's a new brain-computer interface (BCI) system that's opening up real-time, natural conversation for people who can't speak. Instead, it translates the brain signals that would normally control the muscles used for speech, allowing users to "talk" and even "sing" through a computer, almost instantly. Sign up for my FREE CyberGuy Report Get my best tech tips, urgent security alerts, and exclusive deals delivered straight to your inbox. Plus, you'll get instant access to my Ultimate Scam Survival Guide - free when you join my CYBERGUY.COM/NEWSLETTER.

By 2030, rapid technological advancements are expected to reshape humanity, unlocking abilities once confined to science fiction--from superhuman strength to enhanced senses. Robotic exoskeletons may soon allow people to lift heavy objects with ease, while AI-powered wearables, such as smart glasses and earbuds, could provide real-time information and immersive augmented reality experiences. Healthcare may be revolutionized by microscopic nanobots capable of repairing tissue and fighting disease from within the bloodstream, potentially extending human lifespans. Developers are also working on contact lenses with infrared vision and devices that allow users to "feel" digital objects, paving the way for entirely new ways to experience the world. Tech pioneers like former Google engineer Ray Kurzweil believe these innovations are early steps toward the merging of humans and machines, with brain-computer interfaces offering direct access to digital intelligence.

Paradromics is shifting from research to clinical trials. Recently, a neurotech company called Paradromics made headlines by successfully implanting its brain-computer interface (BCI) in a human for the first time. The procedure happened at the University of Michigan during a patient's routine epilepsy surgery. The device was both placed and removed in just about 20 minutes, a quick turnaround for such a complex technology. This achievement is a big deal for Paradromics, which has been working on this brain implant technology for nearly 10 years.

Aligning neural dynamics with movements is a fundamental goal in neuroscience and brain-machine interfaces. However, there is still a lack of dimensionality reduction methods that can effectively align low-dimensional latent dynamics with movements. To address this gap, we propose Neural Embeddings Rank (NER), a technique that embeds neural dynamics into a 3D latent space and contrasts the embeddings based on movement ranks. NER learns to regress continuous representations of neural dynamics (i.e., embeddings) on continuous movements. We apply NER and six other dimensionality reduction techniques to neurons in the primary motor cortex (M1), dorsal premotor cortex (PMd), and primary somatosensory cortex (S1) as monkeys perform reaching tasks.

Neural Information Processing Systems http://nips.cc/

Time-varying linear state-space models are powerful tools for obtaining mathematically interpretable representations of neural signals. For example, switching and decomposed models describe complex systems using latent variables that evolve according to simple locally linear dynamics. However, existing methods for latent variable estimation are not robust to dynamical noise and system nonlinearity due to noise-sensitive inference procedures and limited model formulations. This can lead to inconsistent results on signals with similar dynamics, limiting the model's ability to provide scientific insight. In this work, we address these limitations and propose a probabilistic approach to latent variable estimation in decomposed models that improves robustness against dynamical noise. Additionally, we introduce an extended latent dynamics model to improve robustness against system nonlinearities. We evaluate our approach on several synthetic dynamical systems, including an empirically-derived brain-computer interface experiment, and demonstrate more accurate latent variable inference in nonlinear systems with diverse noise conditions. Furthermore, we apply our method to a real-world clinical neurophysiology dataset, illustrating the ability to identify interpretable and coherent structure where previous models cannot.

Recent advances in neuroscientific experimental techniques have enabled us to simultaneously record the activity of thousands of neurons across multiple brain regions. This has led to a growing need for computational tools capable of analyzing how task-relevant information is represented and communicated between several brain regions. Partial information decompositions (PIDs) have emerged as one such tool, quantifying how much unique, redundant and synergistic information two or more brain regions carry about a task-relevant message. However, computing PIDs is computationally challenging in practice, and statistical issues such as the bias and variance of estimates remain largely unexplored. In this paper, we propose a new method for efficiently computing and estimating a PID definition on multivariate Gaussian distributions. We show empirically that our method satisfies an intuitive additivity property, and recovers the ground truth in a battery of canonical examples, even at high dimensionality. We also propose and evaluate, for the first time, a method to correct the bias in PID estimates at finite sample sizes. Finally, we demonstrate that our Gaussian PID effectively characterizes inter-areal interactions in the mouse brain, revealing higher redundancy between visual areas when a stimulus is behaviorally relevant.

First Neuralink brain implant patient Noland Arbaugh discusses his paralysis, his implant and more on'The Will Cain Show.' Elon Musk's Neuralink brain implants are designed to help individuals with disabilities -- and the implant's first user told Fox News on Friday about the revolutionary technology. Arizona native Noland Arbaugh, the first Neuralink brain implant patient, joined "The Will Cain Show" to discuss how the device has helped him regain control of his life. "I'm just beyond grateful," Arbaugh told Fox News host Will Cain. "It's an incredible privilege to be a part of this." Elon Musk shows off his t-shirt reading "Tech Support" while speaking at the first Cabinet meeting hosted by U.S. President Donald Trump, at the White House in Washington, D.C., Feb. 26, 2025.

Neural Information Processing Systems http://nips.cc/