Class I evidence that in patients with AChRAb+ myasthenia gravis, the addition of amifampridine to pyridostigmine was not superior to treatment with pyridostigmine alone and was associated with a higher incidence of adverse events.
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Galloni et al. introduce “dendritic target propagation”: a Dale’s law-compliant learning algorithm for cortical microcircuits with soma-and dendrite-targeting inhibition and realistic connectivity constraints. By combining experimentally derived BTSP and Hebbian rules, dendrites compute local error proxies via E/I mismatch, supporting gradient-based deep learning during simultaneous bottom-up and top-down signaling.
In the era of big data and artificial intelligence, a new approach has emerged for solving combinatorial optimization problems, which involves finding the most efficient solution among many possible options and can otherwise take thousands of years to compute.
A KAIST research team has developed computational hardware that can be implemented entirely using existing silicon processes, enabling deployment on existing fabrication lines without additional facilities. This is expected to enable faster and more accurate decision-making across various industries, including logistics, finance, and semiconductor design.
The research is published in Science Advances.
There is something universally appealing about the slap bracelet, and the way a simple tap causes it to switch between a straight shape and a curled one. What you probably didn’t know is that a slap bracelet’s satisfying snap is the same principle behind bistable structures. These can toggle between two stable positions (one representing 0 and the other 1) to store data directly within their physical forms as mechanical bits (m-bits).
Because of their exciting potential for efficient control of robotic and other mechanical systems, researchers have been engineering special materials with programmable structures (programmable metamaterials) for years. But until now, actual programming of such systems has been a major challenge: mechanical bits must typically be controlled individually, which is extremely cumbersome and time-consuming.
Now, researchers in the Flexible Structures Laboratory (fleXLab) in EPFL’s School of Engineering, the Dutch research institute AMOLF, and Leiden University have found a way to program metamaterials globally with a surprisingly simple solution: rotation. By tuning a spinning platform’s speed, direction, and acceleration, the researchers can harness forces arising in a rotating system—such as centrifugal and Euler forces—to make elastic beams snap back and forth, creating a simple new way to “write” multiple mechanical bits at once.
Mark Zuckerberg is following a path paved by fellow billionaires Bill Gates and Warren Buffet: laundering his untold billions through a health research prestige project.
Called the Chan Zuckerberg Biohub — his wife Priscilla Chan, a pediatrician, is also involved — the foundation’s stated long-term mission is to “cure and prevent all disease through AI-powered biology, frontier research, and state-of-the-art technology.”
True to those enormous goals, the Biohub recently announced a $500 million investment into AI models of human cells, specifically, in order to “accelerate the cure and prevention of all diseases,” Euronews reported.
Abstract: Understanding how populations of neurons represent information is a central challenge across machine learning and neuroscience. Recent work in both fields has begun to characterize the representational geometry and functionality underlying complex distributed activity. For example, artificial neural networks trained on data with more features than neurons compress data by representing features non-orthogonally in so-called *superposition*. However, the effect of time (or memory), an additional capacity-constraining pressure, on underlying representational geometry in recurrent models is not well understood. Here, we study how memory demands affect representational geometry in recurrent neural networks (RNNs), introducing the concept of temporal superposition. We develop a theoretical framework in RNNs with linear recurrence trained on a delayed serial recall task to better understand how properties of the data, task demands, and network dimensionality lead to different representational strategies, and show that these insights generalize to nonlinear RNNs. Through this, we identify an effectively linear, dense regime and a sparse regime where RNNs utilize an interference-free space, characterized by a phase transition in the angular distribution of features and decrease in spectral radius. Finally, we analyze the interaction of spatial and temporal superposition to observe how RNNs mediate different representational tradeoffs. Overall, our work offers a mechanistic, geometric explanation of representational strategies RNNs learn, how they depend on capacity and task demands, and why.
Supplementary Material: zip
Primary Area: interpretability and explainable AI.
Chemists and computer scientists tapped AI to find new disinfectants to combat the growing threat of dangerous “superbugs.”
The Journal of Chemical Information and Modeling published their computational-experimental framework for developing quaternary ammonium compounds, or QACs, to kill bacteria.
The method yielded 11 new QACs that show activity against antimicrobial-resistant bacteria.
Ever wonder what actually happens inside the AI after you hit “Enter”?
You type a prompt into your favorite generative AI, and within seconds, your screen fills with exactly what you asked for—whether it’s a quarterly report or a cinematic image of a cyberpunk golden retriever. It feels like absolute magic.
But behind that seamless curtain lies a bustling, microscopic economy running entirely on a digital currency you’ve probably heard of but might not fully understand: the token.
Most of us only ever see the input and the output. We don’t see the internal cash register ringing, the mathematical gymnastics, or the sprawling “assembly line” churning through billions of calculations.
What actually happens between the moment you hit send and the moment your final masterpiece appears? In my newest blog post, I peel back the curtain to trace the fascinating journey of an AI token.
I break down this invisible economy—from the “toll booth” of the input phase to the heavy lifting of the output phase—and show you exactly how the machine balances the books.