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Category: biological

Researchers have developed “infomorphic neurons” that learn independently, mimicking their biological counterparts more accurately than previous artificial neurons. A team of researchers from the Göttingen Campus Institute for Dynamics of Biological Networks (CIDBN) at the University of Göttingen and the Max Planck Institute for Dynamics and Self-Organization (MPI-DS) has programmed these infomorphic neurons and constructed artificial neural networks from them.

The special feature is that the individual artificial neurons learn in a self-organized way and draw the necessary information from their immediate environment in the network. Their findings are published in the journal Proceedings of the National Academy of Sciences.

Both the and modern are extremely powerful. At the lowest level, the neurons work together as rather simple computing units.

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Humans like to think that being multicellular (and bigger) is a definite advantage, even though 80% of life on Earth consists of single-celled organisms—some thriving in conditions lethal to any beast.

In fact, why and how multicellular life evolved has long puzzled biologists. The first known instance of multicellularity was about 2.5 billion years ago, when marine cells (cyanobacteria) hooked up to form filamentous colonies. How this transition occurred and the benefits it accrued to the cells, though, is less than clear.

A study originating from the Marine Biological Laboratory (MBL) presents a striking example of cooperative organization among cells as a potential force in the evolution of multicellular life. Based on the fluid dynamics of cooperative feeding by Stentor, a relatively giant unicellular organism, the report is published in Nature Physics.

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Biological systems, once thought too chaotic for quantum effects, may be quietly leveraging quantum mechanics to process information faster than anything man-made.

New research suggests this isn’t just happening in brains, but across all life, including bacteria and plants.

Schrödinger’s legacy inspires a quantum leap.

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Novel artificial neurons learn independently and are more strongly modeled on their biological counterparts. A team of researchers from the Göttingen Campus Institute for Dynamics of Biological Networks (CIDBN) at the University of Göttingen and the Max Planck Institute for Dynamics and Self-Organization (MPI-DS) has programmed these infomorphic neurons and constructed artificial neural networks from them. The special feature is that the individual artificial neurons learn in a self-organized way and draw the necessary information from their immediate environment in the network.

The results were published in PNAS (“A general framework for interpretable neural learning based on local information-theoretic goal functions”).

Both, human brain and modern artificial neural networks are extremely powerful. At the lowest level, the neurons work together as rather simple computing units. An artificial neural network typically consists of several layers composed of individual neurons. An input signal passes through these layers and is processed by artificial neurons in order to extract relevant information. However, conventional artificial neurons differ significantly from their biological models in the way they learn.

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A major breakthrough in liquid catalysis is transforming how essential products are made, making the chemical manufacturing process faster, safer and more sustainable than ever before.

Researchers from Monash University, the University of Sydney, and RMIT University have developed a liquid that could transform chemical production across a range of industries—from pharmaceuticals and sustainable products to advanced materials.

By dissolving palladium in liquid gallium the team, led by Associate Professor Md. Arifur Rahim from Monash University’s Department of Chemical and Biological Engineering, created a self-regenerating catalytic system with unprecedented efficiency.

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Most computers run on microchips, but what if we’ve been overlooking a simpler, more elegant computational tool all this time? In fact, what if we were the computational tool?

As crazy as it sounds, a future in which humans are the ones doing the computing may be closer than we think. In an article published in IEEE Access, Yo Kobayashi from the Graduate School of Engineering Science at the University of Osaka demonstrates that living tissue can be used to process information and solve complex equations, exactly as a computer does.

This achievement is an example of the power of the computational framework known as , in which data are input into a complex “reservoir” that has the ability to encode rich patterns. A computational model then learns to convert these patterns into meaningful outputs via a neural network.

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A joint research team has successfully developed a next-generation soft robot based on liquid. The research was published in Science Advances.

Biological cells possess the ability to deform, freely divide, fuse, and capture foreign substances. Research efforts have long been dedicated to replicating these unique capabilities in artificial systems. However, traditional solid-based robots have faced limitations in effectively mimicking the flexibility and functionality of living cells.

To overcome these challenges, the joint research team successfully developed a particle-armored liquid robot, encased in unusually dense hydrophobic (water-repelling) particles.

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Scientists from TU Delft and EPFL have created a quadruped robot capable of running like a dog without the need for motors. This achievement, a product of combining innovative mechanics with data-driven technology, was published in Nature Machine Intelligence and could pave the way for energy-efficient robotics.

“Commercial quadruped robots are becoming more common, but their energy inefficiency limits their operating time,” explains Cosimo Della Santina, assistant professor at TU Delft. “Our goal was to address this issue by optimizing the robot’s mechanics by mimicking the efficiency of biological systems.”

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