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Comparing brains to computers is a long and dearly held analogy in both neuroscience and computer science.
It’s not hard to see why.
Our brains can perform many of the tasks we want computers to handle with an easy, mysterious grace. So, it goes, understanding the inner workings of our minds can help us build better computers; and those computers can help us better understand our own minds. Also, if brains are like computers, knowing how much computation it takes them to do what they do can help us predict when machines will match minds.
Indeed, there’s already a productive flow of knowledge between the fields.
Deep learning, a powerful form of artificial intelligence, for example, is loosely modeled on the brain’s vast, layered networks of neurons.
You can think of each “node” in a deep neural network as an artificial neuron. Like neurons, nodes receive signals from other nodes connected to them and perform mathematical operations to transform input into output.
Depending on the signals a node receives, it may opt to send its own signal to all the nodes in its network. In this way, signals cascade through layer upon layer of nodes, progressively tuning and sharpening the algorithm.
The brain works like this too. But the keyword above is loosely.
Scientists know biological neurons are more complex than the artificial neurons employed in deep learning algorithms, but it’s an open question just how much more complex.
In a fascinating paper published recently in the journal Neuron, a team of researchers from the Hebrew University of Jerusalem tried to get us a little closer to an answer. While they expected the results would show biological neurons are more complex—they were surprised at just how much more complex they actually are.
In the study, the team found it took a five- to eight-layer neural network, or nearly 1,000 artificial neurons, to mimic the behavior of a single biological neuron from the brain’s cortex.
Though the researchers caution the results are an upper bound for complexity—as opposed to an exact measurement of it—they also believe their findings might help scientists further zero in on what exactly makes biological neurons so complex. And that knowledge, perhaps, can help engineers design even more capable neural networks and AI.
“[The result] forms a bridge from biological neurons to artificial neurons,” Andreas Tolias, a computational neuroscientist at Baylor College of Medicine, told Quanta last week.
Neurons are the cells that make up our brains. There are many different types of neurons, but generally, they have three parts: spindly, branching structures called dendrites, a cell body, and a root-like axon.
On one end, dendrites connect to a network of other neurons at junctures called synapses. At the other end, the axon forms synapses with a different population of neurons. Each cell receives electrochemical signals through its dendrites, filters those signals, and then selectively passes along its own signals (or spikes).
To computationally compare biological and artificial neurons, the team asked: How big of an artificial neural network would it take to simulate the behavior of a single biological neuron?
First, they built a model of a biological neuron (in this case, a pyramidal neuron from a rat’s cortex). The model used some 10,000 differential equations to simulate how and when the neuron would translate a series of input signals into a spike of its own.
They then fed inputs into their simulated neuron, recorded the outputs, and trained deep learning algorithms on all the data. Their goal? Find the algorithm that could most accurately approximate the model.
(Video: A model of a pyramidal neuron (left) receives signals through its dendritic branches. In this case, the signals provoke three spikes.)
They increased the number of layers in the algorithm until it was 99 percent accurate at predicting the simulated neuron’s output given a set of inputs. The sweet spot was at least five layers but no more than eight, or around 1,000 artificial neurons per biological neuron. The deep learning algorithm was much simpler than the original model—but still quite complex.
From where does this complexity arise?
As it turns out, it’s mostly due to a type of chemical receptor in dendrites—the NMDA ion channel—and the branching of dendrites in space. “Take away one of those things, and a neuron turns [into] a simple device,” lead author David Beniaguev tweeted in 2019, describing an earlier version of the work published as a preprint.
Indeed, after removing these features, the team found they could match the simplified biological model with but a single-layer deep learning algorithm.
A Moving Benchmark
It’s tempting to extrapolate the team’s results to estimate the computational complexity of the whole brain. But we’re nowhere near such a measure.
For one, it’s possible the team didn’t find the most efficient algorithm.
It’s common for the the developer community to rapidly improve upon the first version of an advanced deep learning algorithm. Given the intensive iteration in the study, the team is confident in the results, but they also released the model, data, and algorithm to the scientific community to see if anyone could do better.
Also, the model neuron is from a rat’s brain, as opposed to a human’s, and it’s only one type of brain cell. Further, the study is comparing a model to a model—there is, as of yet, no way to make a direct comparison to a physical neuron in the brain. It’s entirely possible the real thing is more, not less, complex.
Still, the team believes their work can push neuroscience and AI forward.
In the former case, the study is further evidence dendrites are complicated critters worthy of more attention. In the latter, it may lead to radical new algorithmic architectures.
Idan Segev, a coauthor on the paper, suggests engineers should try replacing the simple artificial neurons in today’s algorithms with a mini five-layer network simulating a biological neuron. “We call for the replacement of the deep network technology to make it closer to how the brain works by replacing each simple unit in the deep network today with a unit that represents a neuron, which is already—on its own—deep,” Segev said.
Whether so much added complexity would pay off is uncertain. Experts debate how much of the brain’s detail algorithms need to capture to achieve similar or better results.
But it’s hard to argue with millions of years of evolutionary experimentation. So far, following the brain’s blueprint has been a rewarding strategy. And if this work is any indication, future neural networks may well dwarf today’s in size and complexity.
Image Credit: NICHD/S. Jeong Continue reading
Humanoid robots have the potential of assisting humans in a variety of settings, ranging from home environments to malls, schools and healthcare facilities. Some roboticists have been specifically investigating the potential of social robots as tools to offer care and companionship to the elderly population. Continue reading
The little pigs bouncing around the lab looked exceedingly normal. Yet their adorable exterior hid a remarkable secret: each piglet carried two different sets of genes. For now, both sets came from their own species. But one day, one of those sets may be human.
The piglets are chimeras—creatures with intermingled sets of genes, as if multiple entities were seamlessly mashed together. Named after the Greek lion-goat-serpent monsters, chimeras may hold the key to an endless supply of human organs and tissues for transplant. The crux is growing these human parts in another animal—one close enough in size and function to our own.
Last week, a team from the University of Minnesota unveiled two mind-bending chimeras. One was joyous little piglets, each propelled by muscles grown from a different pig. Another was pig embryos, transplanted into surrogate pigs, that developed human muscles for more than 20 days.
The study, led by Drs. Mary and Daniel Garry at the University of Minnesota, had a therapeutic point: engineering a brilliant way to replace muscle loss, especially for the muscles around our skeletons that allow us to move and navigate the world. Trauma and injury, such as from firearm wounds or car crashes, can damage muscle tissue beyond the point of repair. Unfortunately, muscles are also stubborn in that donor tissue from cadavers doesn’t usually “take” at the injury site. For now, there are no effective treatments for severe muscle death, called volumetric muscle loss.
The new human-pig hybrids are designed to tackle this problem. Muscle wasting aside, the study also points to a clever “hack” that increases the amount of human tissue inside a growing pig embryo.
If further improved, the technology could “provide an unlimited supply of organs for transplantation,” said Dr. Mary Garry to Inverse. What’s more, because the human tissue can be sourced from patients themselves, the risk of rejection by the immune system is relatively low—even when grown inside a pig.
“The shortage of organs for heart transplantation, vascular grafting, and skeletal muscle is staggering,” said Garry. Human-animal chimeras could have a “seismic impact” that transforms organ transplantation and helps solve the organ shortage crisis.
That is, if society accepts the idea of a semi-humanoid pig.
The new study took a page from previous chimera recipes.
The main ingredients and steps go like this: first, you need an embryo that lacks the ability to develop a tissue or organ. This leaves an “empty slot” of sorts that you can fill with another set of genes—pig, human, or even monkey.
Second, you need to fine-tune the recipe so that the embryos “take” the new genes, incorporating them into their bodies as if they were their own. Third, the new genes activate to instruct the growing embryo to make the necessary tissue or organs without harming the overall animal. Finally, the foreign genes need to stay put, without cells migrating to another body part—say, the brain.
Not exactly straightforward, eh? The piglets are technological wonders that mix cutting-edge gene editing with cloning technologies.
The team went for two chimeras: one with two sets of pig genes, the other with a pig and human mix. Both started with a pig embryo that can’t make its own skeletal muscles (those are the muscles surrounding your bones). Using CRISPR, the gene-editing Swiss Army Knife, they snipped out three genes that are absolutely necessary for those muscles to develop. Like hitting a bullseye with three arrows simultaneously, it’s already a technological feat.
Here’s the really clever part: the muscles around your bones have a slightly different genetic makeup than the ones that line your blood vessels or the ones that pump your heart. While the resulting pig embryos had severe muscle deformities as they developed, their hearts beat as normal. This means the gene editing cut only impacted skeletal muscles.
Then came step two: replacing the missing genes. Using a microneedle, the team injected a fertilized and slightly developed pig egg—called a blastomere—into the embryo. If left on its natural course, a blastomere eventually develops into another embryo. This step “smashes” the two sets of genes together, with the newcomer filling the muscle void. The hybrid embryo was then placed into a surrogate, and roughly four months later, chimeric piglets were born.
Equipped with foreign DNA, the little guys nevertheless seemed totally normal, nosing around the lab and running everywhere without obvious clumsy stumbles. Under the microscope, their “xenomorph” muscles were indistinguishable from run-of-the-mill average muscle tissue—no signs of damage or inflammation, and as stretchy and tough as muscles usually are. What’s more, the foreign DNA seemed to have only developed into muscles, even though they were prevalent across the body. Extensive fishing experiments found no trace of the injected set of genes inside blood vessels or the brain.
A Better Human-Pig Hybrid
Confident in their recipe, the team next repeated the experiment with human cells, with a twist. Instead of using controversial human embryonic stem cells, which are obtained from aborted fetuses, they relied on induced pluripotent stem cells (iPSCs). These are skin cells that have been reverted back into a stem cell state.
Unlike previous attempts at making human chimeras, the team then scoured the genetic landscape of how pig and human embryos develop to find any genetic “brakes” that could derail the process. One gene, TP53, stood out, which was then promptly eliminated with CRISPR.
This approach provides a way for future studies to similarly increase the efficiency of interspecies chimeras, the team said.
The human-pig embryos were then carefully grown inside surrogate pigs for less than a month, and extensively analyzed. By day 20, the hybrids had already grown detectable human skeletal muscle. Similar to the pig-pig chimeras, the team didn’t detect any signs that the human genes had sprouted cells that would eventually become neurons or other non-muscle cells.
For now, human-animal chimeras are not allowed to grow to term, in part to stem the theoretical possibility of engineering humanoid hybrid animals (shudder). However, a sentient human-pig chimera is something that the team specifically addressed. Through multiple experiments, they found no trace of human genes in the embryos’ brain stem cells 20 and 27 days into development. Similarly, human donor genes were absent in cells that would become the hybrid embryos’ reproductive cells.
Despite bioethical quandaries and legal restrictions, human-animal chimeras have taken off, both as a source of insight into human brain development and a well of personalized organs and tissues for transplant. In 2019, Japan lifted its ban on developing human brain cells inside animal embryos, as well as the term limit—to global controversy. There’s also the question of animal welfare, given that hybrid clones will essentially become involuntary organ donors.
As the debates rage on, scientists are nevertheless pushing the limits of human-animal chimeras, while treading as carefully as possible.
“Our data…support the feasibility of the generation of these interspecies chimeras, which will serve as a model for translational research or, one day, as a source for xenotransplantation,” the team said.
Image Credit: Christopher Carson on Unsplash Continue reading
This research project was originated from the collaboration between two institutions with their respective expertise: The TIPs laboratory of the ULB, in Belgium, which is a group dedicated to the study of transport phenomena and fluid interfaces, and the AS2M department of the FEMTO-ST institute, in France, specialized in microrobotics. And thus, ThermoBot was born, a new kind of manipulation platform working on the air-water interface. ThermoBot uses an original actuation mechanism, an infrared laser that locally heats the air-water interface, triggering so-called thermocapillary flows. Combining our specialties in interfacial phenomena and robotics, we were able to use this flow to displace floating components in a controlled manner. Continue reading