Lifeboat Foundation

An open-source advanced supercomputer algorithm predicts the patterning and dynamics of living materials, allowing for the exploration of their behaviors across space and time.

Biological materials consist of individual components, including tiny motors that transform fuel into motion. This process creates patterns of movement, leading the material to shape itself through coherent flows driven by constant energy consumption. These perpetually driven materials are called “active matter.”

The mechanics of cells and tissues can be described by active matter theory, a scientific framework to understand the shape, flows, and form of living materials. The active matter theory consists of many challenging mathematical equations.

Biological materials are made of individual components, including tiny motors that convert fuel into motion. This creates patterns of movement, and the material shapes itself with coherent flows by constant consumption of energy. Such continuously driven materials are called active matter.

The mechanics of cells and tissues can be described by active matter theory, a scientific framework to understand the shape, flow, and form of living materials. The active matter theory consists of many challenging mathematical equations.

Scientists from the Max Planck Institute of Molecular Cell Biology and Genetics (MPI-CBG) in Dresden, the Center for Systems Biology Dresden (CSBD), and the TU Dresden have now developed an algorithm, implemented in an open-source supercomputer code, that can for the first time solve the equations of active matter theory in realistic scenarios.

Humankind on the verge of evolutionary traps, a new study: …For the first time, scientists have used the concept of evolutionary traps on human societies at large.

For the first time, scientists have used the concept of evolutionary traps on human societies at large. They find that humankind risks getting stuck in 14 evolutionary dead ends, ranging from global climate tipping points to misaligned artificial intelligence, chemical pollution, and accelerating infectious diseases.

The evolution of humankind has been an extraordinary success story. But the Anthropocene—the proposed geological epoch shaped by us humans—is showing more and more cracks. Multiple global crises, such as the COVID-19 pandemic, climate change, food insecurity, financial crises, and conflicts have started to occur simultaneously in something which scientists refer to as a polycrisis.

Continue reading “New research maps 14 potential evolutionary dead ends for humanity and ways to avoid them” »

A fundamental question in neuroscience is what are the constraints that shape the structural and functional organization of the brain. By bringing biological cost constraints into the optimization process of artificial neural networks, Achterberg, Akarca and colleagues uncover the joint principle underlying a large set of neuroscientific findings.

The human mind is by far one of the most amazing natural phenomena known to man. It embodies our perception of reality, and is in that respect the ultimate observer. The past century produced monumental discoveries regarding the nature of nerve cells, the anatomical connections between nerve cells, the electrophysiological properties of nerve cells, and the molecular biology of nervous tissue. What remains to be uncovered is that essential something – the fundamental dynamic mechanism by which all these well understood biophysical elements combine to form a mental state. In this chapter, we further develop the concept of an intraneuronal matrix as the basis for autonomous, self–organized neural computing, bearing in mind that at this stage such models are speculative. The intraneuronal matrix – composed of microtubules, actin filaments, and cross–linking, adaptor, and scaffolding proteins – is envisioned to be an intraneuronal computational network, which operates in conjunction with traditional neural membrane computational mechanisms to provide vastly enhanced computational power to individual neurons as well as to larger neural networks. Both classical and quantum mechanical physical principles may contribute to the ability of these matrices of cytoskeletal proteins to perform computations that regulate synaptic efficacy and neural response. A scientifically plausible route for controlling synaptic efficacy is through the regulation of neural transport of synaptic proteins and of mRNA. Operations within the matrix of cytoskeletal proteins that have applications to learning, memory, perception, and consciousness, and conceptual models implementing classical and quantum mechanical physics are discussed. Nanoneuroscience methods are emerging that are capable of testing aspects of these conceptual models, both theoretically and experimentally. Incorporating intra–neuronal biophysical operations into existing theoretical frameworks of single neuron and neural network function stands to enhance existing models of neurocognition.

Summary: Researchers developed an experimental computing system, resembling a biological brain, that successfully identified handwritten numbers with a 93.4% accuracy rate.

This breakthrough was achieved using a novel training algorithm providing continuous real-time feedback, outperforming traditional batch data processing methods which yielded 91.4% accuracy.

The system’s design features a self-organizing network of nanowires on electrodes, with memory and processing capabilities interwoven, unlike conventional computers with separate modules.

An experimental computing system physically modeled after the biological brain has “learned” to identify handwritten numbers with an overall accuracy of 93.4%. The key innovation in the experiment was a new training algorithm that gave the system continuous information about its success at the task in real time while it learned. The study was published in Nature Communications.

The algorithm outperformed a conventional machine-learning approach in which training was performed after a batch of data had been processed, producing 91.4% accuracy. The researchers also showed that memory of past inputs stored in the system itself enhanced learning. In contrast, other computing approaches store memory within software or hardware separate from a device’s processor.

For 15 years, researchers at the California NanoSystems Institute at UCLA, or CNSI, have been developing a new platform technology for computation. The technology is a brain-inspired system composed of a tangled-up network of wires containing silver, laid on a bed of electrodes. The system receives input and produces output via pulses of electricity. The individual wires are so small that their diameter is measured on the nanoscale, in billionths of a meter.

If you have any form of Arachnophobia, do not read this article. You’ve been warned. Now if you’re like me and have a mad respect for Mother Nature, I posit you this query. Did you know that spiders can fly? And not by the way you may think.

Good news for your nightmares: Spiders can fly. Despite not having wings, new research shows that spiders have the ability to propel themselves using the Earth’s electric field, with little to no help from wind or webs. Because humans can’t feel these electric currents, their role in biology can often go ignored. But if electrostatic is what is helping spiders fly more than two miles high in the air, let’s pay attention.

In a study published in Current Biology on Thursday, Drs. Erica L. Morley and Daniel Robert of the University of Bristol found that when spiders are placed in a chamber with no wind but a small electric field, they were still able to to fly, despite the prevailing idea that a spider’s flight was reliant on wind currents.

Continue reading “Spiders Can Fly and They Don’t Even Need Wings, Study Claims” »

In October, a paper titled “Assembly theory explains and quantifies selection and evolution” appeared in the journal Nature. The authors—a team led by Lee Cronin at the University of Glasgow and Sara Walker at Arizona State University—claim their theory is an “interface between physics and biology” which explains how complex biological forms can evolve.

The paper provoked strong responses. On the one hand were headlines like “Bold New ” Theory of Everything’ Could Unite Physics And Evolution”

On the other were reactions from scientists. One evolutionary biologist tweeted after multiple reads I still have absolutely no idea what [this paper] is doing. Another said I read the paper and I feel more confused […] I think reading that paper has made me forget my own name.