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Category: biological

‘Collapsible scissored surfaces’ complete trilogy of metamaterial design principles

Over the past decade, Professor L. Mahadevan’s Soft Math Lab at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS) has helped establish how the ancient Japanese paper arts of folding or cutting can be used to inversely design structures that transform dramatically in shape and function. Now, the researchers have created a new class of shape-changing matter, based not on folds or cuts, but linkages—networks of interconnected scissor mechanisms that collapse into lines and deploy into curved surfaces.

The study published in the Proceedings of the National Academy of Sciences, led by physics graduate student Noah Toyonaga, establishes a mathematical and physical framework for what the authors call collapsible scissored surfaces—deployable lattices of two-bar linkages that can transform from a one-dimensional collapsed state into two-dimensional structures with prescribed geometry.

“Origami showed how folds can encode shape,” said senior author Mahadevan, the Lola England de Valpine Professor of Applied Mathematics, of Organismic and Evolutionary Biology, and of Physics. “Kirigami showed how cuts can unlock motion and functionality. This work asks a complementary question: What can be achieved when the basic building block is not a fold or a cut, but a linkage?”

Brain-inspired AI architecture could compute faster while using far less power

Spiking neural networks (SNNs) are artificial intelligence (AI) models inspired by how biological neurons communicate with each other. While biological neurons exchange information in the form of electrical impulses, SNNs rely on brief signals known as spikes.

SNNs have proved promising for reducing power consumption, as developers can ensure they do not process information continuously, but rather only when meaningful changes occur. This could be highly advantageous, as current AI systems are known to consume large amounts of energy.

While some SNNs introduced in the past achieved encouraging results, they typically struggle to retain useful information (i.e., context) for long periods. This was found to be particularly challenging when the models have only a limited amount of data storage available or are operating under energy constraints.

Tracy R. Atkins on Aeternum Ray: Don’t Wait For The Singularity

“Don’t Wait For The Singularity.”

That was Tracy R. Atkins’ message when I sat down with him 14 years ago, and it lands harder now than it did then.

While almost every story about #ArtificialIntelligence was busy imagining the apocalypse, Tracy wrote a novel that flatly refused to. Aeternum Ray is unapologetically utopian: a series of letters from a 240-year-old father to his newborn son, looking back across centuries of love, loss, and a world watched over by an AI named Ray.

In our conversation, we get into what the #Singularity actually means to him, why he chose to write utopia when dystopia sells, whether humanity’s future is digital or whether biology still matters, and the uncomfortable question of whether we even survive the road to get there.

Fourteen years on, the technology has caught up to much of what we talked about. The harder question is whether our reasons for building it have, and that is the part I keep coming back to.

So is openly imagining a good future naive, or is it the most radical thing a #futurist can do? Watch the interview and decide for yourself.

Continue reading “Tracy R. Atkins on Aeternum Ray: Don’t Wait For The Singularity” | >

Memory, agency, and learning in biological and AI systems with Michael Levin and Katrina Schleisman

What if memory isn’t storage at all — but a message from your past self that has to be interpreted?

In this episode, biologist Michael Levin and cognitive neuroscientist Katrina Schleisman join me to take apart one of the most quietly broken ideas in science and AI: that memory works like a hard drive. It doesn’t. And once you see why, a lot of assumptions about minds, machines, and what it means to \.

How to Build a Synthethic Mind: Brain Inspired AI Exists Now

Further Reading.
Thumbnail image credit: Adobe Stock.

Brains and algorithms partially converge in natural language processing.
https://www.nature.com/articles/s4200…

Strong Prediction: Language Model Surprisal Explains Multiple N400 Effects.
https://pmc.ncbi.nlm.nih.gov/articles…

Foundation model of neural activity predicts response to new stimulus types.
https://www.nature.com/articles/s4158…

Dendrites endow artificial neural networks with accurate, robust and parameter-efficient learning.
https://www.nature.com/articles/s4146…

A Computational Perspective on NeuroAI and Synthetic Biological Intelligence.

Continue reading “How to Build a Synthethic Mind: Brain Inspired AI Exists Now” | >

Brain enzyme caught doing something unexpected—it builds polysialic acid on itself

A chance discovery at Nagoya University in Japan has shown that a well-known brain enzyme has a hidden ability: It builds a sugar chain on itself, becomes secreted from the cell and deactivates, then switches on outside the cell once the chain is removed. The finding, published in the Journal of Biological Chemistry, overturns a decades-old assumption about how polysialic acid, a sugar chain critical for brain development and function, is produced and shows a new way an enzyme can regulate its own activity.

The human brain is covered in sugar chains, or glycans, molecular structures that coat cells and regulate how they communicate. One of the most important is polysialic acid, a long chain found mainly in the brain.

Polysialic acid keeps brain cells from adhering too tightly to each other and binds to growth factors and neurotrophins to regulate the presentation of their receptors. Through this, it plays a key role in learning, memory and neural development. Importantly, these sugar chains change rapidly in response to brain activity. The ability to restore them quickly is thought to be essential for normal brain function.

Restless legs syndrome—zebrafish reveal a cerebellar connection

An irresistible urge to move the legs or other areas, often accompanied by unpleasant sensations at night or during rest: Restless Legs Syndrome (RLS) affects millions of people worldwide. Despite being one of the most common sleep-related disorders, its biological causes remain poorly understood.

Researchers led by Professor Alex Schier at the Biozentrum of the University of Basel have discovered new clues about the underlying brain regions and mechanisms. Surprisingly, their findings come from an unlikely model organism: larval zebrafish.

“Studies in humans have implicated many different brain regions, but it remains unclear how they relate to RLS,” says Schier. “Our work highlights possible contributions from the cerebellum, a brain region crucial for coordinating movement.”

Diamond-based particle detector captures one-picosecond electron bursts for high-rate beam diagnostics

Physicists at UC Santa Cruz and other institutes across California and New Mexico have developed a detection system that will allow next-generation particle accelerators to better reveal fundamental biological and chemical processes, as well as advance critical areas such as materials science and energy research.

The Advanced Accelerator Diagnostics Collaboration, a group of two University of California campuses and three U.S. national laboratories, came together to solve a growing need for high-rate beam diagnostics. These accelerators will now jump from 120 pulses a second to 1 million pulses a second, straining current beam diagnostic systems. The results are now published in the journal Physical Review Accelerators and Beams.

“It really highlights the power of collaboration between universities and national laboratories,” said Bruce Schumm, the Long Family Professor of Experimental Physics. “If you took away Lawrence Berkeley Lab, if you took away Los Alamos, if you took away UC Davis, any of those, the whole thing would have fallen apart.”

How to train your magnet: Excitons as a new knob for magnetic control

Scientists can learn a lot about a quantum material by watching how it responds to light. In magnetic semiconductors, one especially useful messenger is the exciton: a pairing of a negatively charged electron and the positively charged “hole” it leaves behind. Until now, excitons in magnetic materials have mostly been used as reporters. They could reveal how spins were arranged or how magnetic waves moved through a material. But Cornell researchers have shown that excitons can do more than observe magnetism. They can actively steer it.

In the paper “Excitonic Spin Torque in a Magnetic Semiconductor,” published June 15 in Nature Materials, Youn Jue (Eunice) Bae, assistant professor of chemistry and chemical biology in the College of Arts and Sciences, and colleagues report that excitons created by light can exert a spin torque in the two-dimensional magnetic semiconductor chromium sulfide bromide, or CrSBr. The finding establishes excitons as a new way to control magnetic motion with light.

“Excitons have been very useful for watching what spins are doing in magnetic materials,” Bae said. “What we show here is that excitons can also act back on the spins. They are not just spectators; they can help drive the magnetic motion.”

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