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AI system self-organizes to develop features of brains of complex organisms

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AI system self-organizes to develop features of brains of complex organisms


Cambridge scientists have shown that placing physical constraints on an artificially-intelligent system — in much the same way that the human brain has to develop and operate within physical and biological constraints — allows it to develop features of the brains of complex organisms in order to solve tasks.

As neural systems such as the brain organise themselves and make connections, they have to balance competing demands. For example, energy and resources are needed to grow and sustain the network in physical space, while at the same time optimising the network for information processing. This trade-off shapes all brains within and across species, which may help explain why many brains converge on similar organisational solutions.

Jascha Achterberg, a Gates Scholar from the Medical Research Council Cognition and Brain Sciences Unit (MRC CBSU) at the University of Cambridge said: “Not only is the brain great at solving complex problems, it does so while using very little energy. In our new work we show that considering the brain’s problem solving abilities alongside its goal of spending as few resources as possible can help us understand why brains look like they do.”

Co-lead author Dr Danyal Akarca, also from the MRC CBSU, added: “This stems from a broad principle, which is that biological systems commonly evolve to make the most of what energetic resources they have available to them. The solutions they come to are often very elegant and reflect the trade-offs between various forces imposed on them.”

In a study published today in Nature Machine Intelligence, Achterberg, Akarca and colleagues created an artificial system intended to model a very simplified version of the brain and applied physical constraints. They found that their system went on to develop certain key characteristics and tactics similar to those found in human brains.

Instead of real neurons, the system used computational nodes. Neurons and nodes are similar in function, in that each takes an input, transforms it, and produces an output, and a single node or neuron might connect to multiple others, all inputting information to be computed.

In their system, however, the researchers applied a ‘physical’ constraint on the system. Each node was given a specific location in a virtual space, and the further away two nodes were, the more difficult it was for them to communicate. This is similar to how neurons in the human brain are organised.

The researchers gave the system a simple task to complete — in this case a simplified version of a maze navigation task typically given to animals such as rats and macaques when studying the brain, where it has to combine multiple pieces of information to decide on the shortest route to get to the end point.

One of the reasons the team chose this particular task is because to complete it, the system needs to maintain a number of elements — start location, end location and intermediate steps — and once it has learned to do the task reliably, it is possible to observe, at different moments in a trial, which nodes are important. For example, one particular cluster of nodes may encode the finish locations, while others encode the available routes, and it is possible to track which nodes are active at different stages of the task.

Initially, the system does not know how to complete the task and makes mistakes. But when it is given feedback it gradually learns to get better at the task. It learns by changing the strength of the connections between its nodes, similar to how the strength of connections between brain cells changes as we learn. The system then repeats the task over and over again, until eventually it learns to perform it correctly.

With their system, however, the physical constraint meant that the further away two nodes were, the more difficult it was to build a connection between the two nodes in response to the feedback. In the human brain, connections that span a large physical distance are expensive to form and maintain.

When the system was asked to perform the task under these constraints, it used some of the same tricks used by real human brains to solve the task. For example, to get around the constraints, the artificial systems started to develop hubs — highly connected nodes that act as conduits for passing information across the network.

More surprising, however, was that the response profiles of individual nodes themselves began to change: in other words, rather than having a system where each node codes for one particular property of the maze task, like the goal location or the next choice, nodes developed a flexible coding scheme. This means that at different moments in time nodes might be firing for a mix of the properties of the maze. For instance, the same node might be able to encode multiple locations of a maze, rather than needing specialised nodes for encoding specific locations. This is another feature seen in the brains of complex organisms.

Co-author Professor Duncan Astle, from Cambridge’s Department of Psychiatry, said: “This simple constraint — it’s harder to wire nodes that are far apart — forces artificial systems to produce some quite complicated characteristics. Interestingly, they are characteristics shared by biological systems like the human brain. I think that tells us something fundamental about why our brains are organised the way they are.”

Understanding the human brain

The team are hopeful that their AI system could begin to shed light on how these constraints, shape differences between people’s brains, and contribute to differences seen in those that experience cognitive or mental health difficulties.

Co-author Professor John Duncan from the MRC CBSU said: “These artificial brains give us a way to understand the rich and bewildering data we see when the activity of real neurons is recorded in real brains.”

Achterberg added: “Artificial ‘brains’ allow us to ask questions that it would be impossible to look at in an actual biological system. We can train the system to perform tasks and then play around experimentally with the constraints we impose, to see if it begins to look more like the brains of particular individuals.”

Implications for designing future AI systems

The findings are likely to be of interest to the AI community, too, where they could allow for the development of more efficient systems, particularly in situations where there are likely to be physical constraints.

Dr Akarca said: “AI researchers are constantly trying to work out how to make complex, neural systems that can encode and perform in a flexible way that is efficient. To achieve this, we think that neurobiology will give us a lot of inspiration. For example, the overall wiring cost of the system we’ve created is much lower than you would find in a typical AI system.”

Many modern AI solutions involve using architectures that only superficially resemble a brain. The researchers say their works shows that the type of problem the AI is solving will influence which architecture is the most powerful to use.

Achterberg said: “If you want to build an artificially-intelligent system that solves similar problems to humans, then ultimately the system will end up looking much closer to an actual brain than systems running on large compute cluster that specialise in very different tasks to those carried out by humans. The architecture and structure we see in our artificial ‘brain’ is there because it is beneficial for handling the specific brain-like challenges it faces.”

This means that robots that have to process a large amount of constantly changing information with finite energetic resources could benefit from having brain structures not dissimilar to ours.

Achterberg added: “Brains of robots that are deployed in the real physical world are probably going to look more like our brains because they might face the same challenges as us. They need to constantly process new information coming in through their sensors while controlling their bodies to move through space towards a goal. Many systems will need to run all their computations with a limited supply of electric energy and so, to balance these energetic constraints with the amount of information it needs to process, it will probably need a brain structure similar to ours.”

The research was funded by the Medical Research Council, Gates Cambridge, the James S McDonnell Foundation, Templeton World Charity Foundation and Google DeepMind.



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‘Dancing molecules’ heal cartilage damage

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AI system self-organizes to develop features of brains of complex organisms


In November 2021, Northwestern University researchers introduced an injectable new therapy, which harnessed fast-moving “dancing molecules,” to repair tissues and reverse paralysis after severe spinal cord injuries.

Now, the same research group has applied the therapeutic strategy to damaged human cartilage cells. In the new study, the treatment activated the gene expression necessary to regenerate cartilage within just four hours. And, after only three days, the human cells produced protein components needed for cartilage regeneration.

The researchers also found that, as the molecular motion increased, the treatment’s effectiveness also increased. In other words, the molecules’ “dancing” motions were crucial for triggering the cartilage growth process.

The study was published today (July 26) in the Journal of the American Chemical Society.

“When we first observed therapeutic effects of dancing molecules, we did not see any reason why it should only apply to the spinal cord,” said Northwestern’s Samuel I. Stupp, who led the study. “Now, we observe the effects in two cell types that are completely disconnected from one another — cartilage cells in our joints and neurons in our brain and spinal cord. This makes me more confident that we might have discovered a universal phenomenon. It could apply to many other tissues.”

An expert in regenerative nanomedicine, Stupp is Board of Trustees Professor of Materials Science and Engineering, Chemistry, Medicine and Biomedical Engineering at Northwestern, where he is founding director of the Simpson Querrey Institute for BioNanotechnology and its affiliated center, the Center for Regenerative Nanomedicine. Stupp has appointments in the McCormick School of Engineering, Weinberg College of Arts and Sciences and Feinberg School of Medicine. Shelby Yuan, a graduate student in the Stupp laboratory, was primary author of the study.

Big problem, few solutions

As of 2019, nearly 530 million people around the globe were living with osteoarthritis, according to the World Health Organization. A degenerative disease in which tissues in joints break down over time, osteoarthritis is a common health problem and leading cause of disability.

In patients with severe osteoarthritis, cartilage can wear so thin that joints essentially transform into bone on bone — without a cushion between. Not only is this incredibly painful, patients’ joints also can no longer properly function. At that point, the only effective treatment is a joint replacement surgery, which is expensive and invasive.

“Current treatments aim to slow disease progression or postpone inevitable joint replacement,” Stupp said. “There are no regenerative options because humans do not have an inherent capacity to regenerate cartilage in adulthood.”

What are ‘dancing molecules’?

Stupp and his team posited that “dancing molecules” might encourage the stubborn tissue to regenerate. Previously invented in Stupp’s laboratory, dancing molecules are assemblies that form synthetic nanofibers comprising tens to hundreds of thousands of molecules with potent signals for cells. By tuning their collective motions through their chemical structure, Stupp discovered the moving molecules could rapidly find and properly engage with cellular receptors, which also are in constant motion and extremely crowded on cell membranes.

Once inside the body, the nanofibers mimic the extracellular matrix of the surrounding tissue. By matching the matrix’s structure, mimicking the motion of biological molecules and incorporating bioactive signals for the receptors, the synthetic materials are able to communicate with cells.

“Cellular receptors constantly move around,” Stupp said. “By making our molecules move, ‘dance’ or even leap temporarily out of these structures, known as supramolecular polymers, they are able to connect more effectively with receptors.”

Motion matters

In the new study, Stupp and his team looked to the receptors for a specific protein critical for cartilage formation and maintenance. To target this receptor, the team developed a new circular peptide that mimics the bioactive signal of the protein, which is called transforming growth factor beta-1 (TGFb-1).

Then, the researchers incorporated this peptide into two different molecules that interact to form supramolecular polymers in water, each with the same ability to mimic TGFb-1. The researchers designed one supramolecular polymer with a special structure that enabled its molecules to move more freely within the large assemblies. The other supramolecular polymer, however, restricted molecular movement.

“We wanted to modify the structure in order to compare two systems that differ in the extent of their motion,” Stupp said. “The intensity of supramolecular motion in one is much greater than the motion in the other one.”

Although both polymers mimicked the signal to activate the TGFb-1 receptor, the polymer with rapidly moving molecules was much more effective. In some ways, they were even more effective than the protein that activates the TGFb-1 receptor in nature.

“After three days, the human cells exposed to the long assemblies of more mobile molecules produced greater amounts of the protein components necessary for cartilage regeneration,” Stupp said. “For the production of one of the components in cartilage matrix, known as collagen II, the dancing molecules containing the cyclic peptide that activates the TGF-beta1 receptor were even more effective than the natural protein that has this function in biological systems.”

What’s next?

Stupp’s team is currently testing these systems in animal studies and adding additional signals to create highly bioactive therapies.

“With the success of the study in human cartilage cells, we predict that cartilage regeneration will be greatly enhanced when used in highly translational pre-clinical models,” Stupp said. “It should develop into a novel bioactive material for regeneration of cartilage tissue in joints.”

Stupp’s lab is also testing the ability of dancing molecules to regenerate bone — and already has promising early results, which likely will be published later this year. Simultaneously, he is testing the molecules in human organoids to accelerate the process of discovering and optimizing therapeutic materials.

Stupp’s team also continues to build its case to the Food and Drug Administration, aiming to gain approval for clinical trials to test the therapy for spinal cord repair.

“We are beginning to see the tremendous breadth of conditions that this fundamental discovery on ‘dancing molecules’ could apply to,” Stupp said. “Controlling supramolecular motion through chemical design appears to be a powerful tool to increase efficacy for a range of regenerative therapies.”



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New understanding of fly behavior has potential application in robotics, public safety

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Why do flies buzz around in circles when the air is still? And why does it matter?

In a paper published online July 26, 2024 by the scientific journal Current Biology, University of Nevada, Reno Assistant Professor Floris van Breugel and Postdoctoral Researcher S. David Stupski respond to this up-until-now unanswered question. And that answer could hold a key to public safety — specifically, how to better train robotic systems to track chemical leaks.

“We don’t currently have robotic systems to track odor or chemical plumes,” van Breugel said. “We don’t know how to efficiently find the source of a wind-borne chemical. But insects are remarkably good at tracking chemical plumes, and if we really understood how they do it, maybe we could train inexpensive drones to use a similar process to find the source of chemicals and chemical leaks.”

A fundamental challenge in understanding how insects track chemical plumes — basically, how does the fly find the banana in your kitchen? — is that wind and odors can’t be independently manipulated.

To address this challenge, van Breugel and Stupski used a new approach that makes it possible to remotely control neurons — specifically the “smell” neurons — on the antennae of flying fruit flies by genetically introducing light-sensitive proteins, an approach called optogenetics. These experiments, part of a $450,000 project funded through the Air Force Office of Scientific Research, made it possible to give flies identical virtual smell experiences in different wind conditions.

What van Breugel and Stupski wanted to know: how do flies find an odor when there’s no wind to carry it? This is, after all, likely the wind experience of a fly looking for a banana in your kitchen. The answer is in the Current Biology article, “Wind Gates Olfaction Driven Search States in Free Flight.” The print version will appear in the Sept. 9 issue.

Flies use environmental cues to detect and respond to air currents and wind direction to find their food sources, according to van Breugel. In the presence of wind, those cues trigger an automatic “cast and surge” behavior, in which the fly surges into the wind after encountering a chemical plume (indicating food) and then casts — moves side to side — when it loses the scent. Cast-and-surge behavior long has been understood by scientists but, according to van Breugel, it was fundamentally unknown how insects searched for a scent in still air.

Through their work, van Breugel and Stupski uncovered another automatic behavior, sink and circle, which involves lowering altitude and repetitive, rapid turns in a consistent direction. Flies perform this innate movement consistently and repetitively, even more so than cast-and-surge behavior.

According to van Breugel, the most exciting aspect of this discovery is that it shows flying flies are clearly able to assess the conditions of the wind — its presence, and direction — before deploying a strategy that works well under these conditions. The fact that they can do this is actually quite surprising — can you tell if there is a gentle breeze if you stick your head out of the window of a moving car? Flies aren’t just reacting to an odor with the same preprogrammed response every time like a simple robot, they are responding in context-appropriate manner. This knowledge potentially could be applied to train more sophisticated algorithms for scent-detecting drones to find the source of chemical leaks.

So, the next time you try to swat a fly in your home, consider the fact that flies might actually be a little more aware of some of their natural surroundings than you are. And maybe just open a window to let it out.



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Lampreys possess a ‘jaw-dropping’ evolutionary origin

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AI system self-organizes to develop features of brains of complex organisms


One of just two vertebrates without a jaw, sea lampreys that are wreaking havoc in Midwestern fisheries are simultaneously helping scientists understand the origins of two important stem cells that drove the evolution of vertebrates.

Northwestern University biologists have pinpointed when the gene network that regulates these stem cells may have evolved and gained insights into what might be responsible for lampreys’ missing mandibles.

The two cell types — pluripotent blastula cells (or embryonic stem cells) and neural crest cells — are both “pluripotent,” which means they can become all other cell types in the body.

In a new paper, researchers compared lamprey genes to those of the Xenopus, a jawed aquatic frog. Using comparative transcriptomics, the study revealed a strikingly similar pluripotency gene network across jawless and jawed vertebrates, even at the level of transcript abundance for key regulatory factors.

But the researchers also discovered a key difference. While both species’ blastula cells express the pou5 gene, a key stem cell regulator, the gene is not expressed in neural crest stem cells in lampreys. Losing this factor may have limited the ability of neural crest cells to form cell types found in jawed vertebrates (animals with spines) that make up the head and jaw skeleton.

The study will be published July 26 in the journal Nature Ecology & Evolution.

By comparing the biology of jawless and jawed vertebrates, researchers can gain insight into the evolutionary origins of features that define vertebrate animals including humans, how differences in gene expression contribute to key differences in the body plan, and what the common ancestor of all vertebrates looked like.

“Lampreys may hold the key to understanding where we came from,” said Northwestern’s Carole LaBonne, who led the study. “In evolutionary biology, if you want to understand where a feature came from, you can’t look forward to more complex vertebrates that have been evolving independently for 500 million years. You need to look backwards to whatever the most primitive version of the type of animal you’re studying is, which leads us back to hagfish and lampreys — the last living examples of jawless vertebrates.”

An expert in developmental biology, LaBonne is a professor of molecular biosciences in the Weinberg College of Arts and Sciences. She holds the Erastus Otis Haven Chair and is part of the leadership of the National Science Foundation’s (NSF) new Simons National Institute for Theory and Mathematics in Biology.

LaBonne and her colleagues previously demonstrated that the developmental origin of neural crest cells was linked to retaining the gene regulatory network that controls pluripotency in blastula stem cells. In the new study, they explored the evolutionary origin of the links between these two stem cell populations.

“Neural crest stem cells are like an evolutionary Lego set,” said LaBonne. “They become wildly different types of cells, including neurons and muscle, and what all those cell types have in common is a shared developmental origin within the neural crest.”

While blastula stage embryonic stem cells lose their pluripotency and become confined to distinct cell types fairly rapidly as an embryo develops, neural crest cells hold onto the molecular toolkit that controls pluripotency later into development.

LaBonne’s team found a completely intact pluripotency network within lamprey blastula cells, stem cells whose role within jawless vertebrates had been an open question. This implies that blastula and neural crest stem cell populations of jawed and jawless vertebrates co-evolved at the base of vertebrates.

Northwestern postdoctoral fellow and first author Joshua York observed “more similarities than differences” between the lamprey and Xenopus.

“While most of the genes controlling pluripotency are expressed in the lamprey neural crest, the expression of one of these key genes — pou5 — was lost from these cells,” York said. “Amazingly, even though pou5 isn’t expressed in a lamprey’s neural crest, it could promote neural crest formation when we expressed it in frogs, suggesting this gene is part of an ancient pluripotency network that was present in our earliest vertebrate ancestors.”

The experiment also helped them hypothesize that the gene was specifically lost in certain creatures, not something jawed vertebrates developed later on.

“Another remarkable finding of the study is that even though these animals are separated by 500 million years of evolution, there are stringent constraints on expression levels of genes needed to promote pluripotency.” LaBonne said. “The big unanswered question is, why?”

The paper was funded by the National Institutes of Health (grants R01GM116538 and F32DE029113), the NSF (grant 1764421), the Simons Foundation (grant SFARI 597491-RWC) and the Walder Foundation through the Life Sciences Research Foundation. The study is dedicated to the memory of Dr. Joseph Walder.



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