Rice behaves in an unexpected way under pressure. When compressed quickly, it becomes weaker, but under slow pressure it stays strong. This insight is helping scientists develop a new material that could be used in “soft” robots that automatically adjust stiffness, as well as protective gear that responds to how fast an impact occurs.
Using this property, researchers created a new type of “metamaterial,” an engineered structure designed to exhibit behaviors not found in natural materials.
These robots are smaller than a strand of human hair but can move independently even without a motor and sensors.
The Machine Intelligence from Cortical Networks (MICrONS) program seeks to revolutionize machine learning by reverse-engineering the algorithms of the brain. It is an ambitious program to map the function and connectivity of cortical circuits, using high throughput imaging technologies, with the goal of providing insights into the computational principles that underlie cortical function in order to advance the next generation of machine learning algorithms.
This website serves as a data portal to release connectivity and functional imaging data collected by a consortium of laboratories led by groups at the Allen Institute for Brain Science, Princeton University, and Baylor College of Medicine, with support from a broad array of teams, coordinated and funded by the IARPA MICrONS program. These data include large scale electron microscopy based reconstructions of cortical circuitry from mouse visual cortex, with corresponding functional imaging data from those same neurons.
Have a Scientific Request? Check out the Virtual Observatory of the Cortex (VORTEX) project, a BRAIN Initiative funded program to bring the MICrONS dataset to the research community. Access proofreading resources to answer your scientific questions.
Tumor tissue engineering has opened new avenues for cancer research. With an emphasis on gastrointestinal malignancies, we summarize capabilities and limitations of patient-derived and engineered organoid models. We then discuss how innovations in biomaterial design, biofabrication, microfluidics, benchmarking, and AI converge to better emulate tumor tissues and advance translational modeling.
Timestamps:
00:00 — New Way Of Computing
06:46 — How It Works
09:39 — Outlook.
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Luke Dicken, who previously spent a decade with Zynga, says his time with the company “has come to an end”
Can a handful of atoms outperform a much larger digital neural network on a real-world task? The answer may be yes. In a study published in Physical Review Letters, a team led by Prof. Peng Xinhua and Assoc. Prof. Li Zhaokai from the University of Science and Technology of China of the Chinese Academy of Sciences demonstrated that a quantum processor comprising just nine interacting spins outperforms classical networks with thousands of nodes in realistic weather forecasting tasks.
By exploiting unique quantum features such as superposition and entanglement, quantum devices offer new ways to represent and process information.
Recent experiments have shown their advantages in specialized benchmark tasks, but extending these gains to real-world applications remains a challenge. In particular, many quantum approaches rely on complex circuits that are difficult to implement accurately on today’s noisy hardware.
In a recent study published in Nature Communications, researchers created a memristor that uses a built-in oxygen gradient to produce slow, stable conductance changes, enabling a reinforcement learning (RL) algorithm to learn faster and more stably than conventional approaches.
Reinforcement learning stands as one of the most promising ways to achieve continual learning in AI. The idea is to replicate how biological systems acquire and adapt knowledge slowly over time. The brain achieves this via ion gradients that regulate slow, directional signaling across cell membranes. Replicating this in hardware is a key goal of neuromorphic computing.
With their ability to mimic synaptic behavior, memristors have long been considered strong candidates for this. However, most existing devices suffer from unpredictable, abrupt conductance changes, making sustained and stable learning difficult.