Tag Archives: neuroscience
#439042 How Scientists Used Ultrasound to Read ...
Thanks to neural implants, mind reading is no longer science fiction.
As I’m writing this sentence, a tiny chip with arrays of electrodes could sit on my brain, listening in on the crackling of my neurons firing as my hands dance across the keyboard. Sophisticated algorithms could then decode these electrical signals in real time. My brain’s inner language to plan and move my fingers could then be used to guide a robotic hand to do the same. Mind-to-machine control, voilà!
Yet as the name implies, even the most advanced neural implant has a problem: it’s an implant. For electrodes to reliably read the brain’s electrical chatter, they need to pierce through the its protective membrane and into brain tissue. Danger of infection aside, over time, damage accumulates around the electrodes, distorting their signals or even rendering them unusable.
Now, researchers from Caltech have paved a way to read the brain without any physical contact. Key to their device is a relatively new superstar in neuroscience: functional ultrasound, which uses sound waves to capture activity in the brain.
In monkeys, the technology could reliably predict their eye movement and hand gestures after just a single trial—without the usual lengthy training process needed to decode a movement. If adopted by humans, the new mind-reading tech represents a triple triumph: it requires minimal surgery and minimal learning, but yields maximal resolution for brain decoding. For people who are paralyzed, it could be a paradigm shift in how they control their prosthetics.
“We pushed the limits of ultrasound neuroimaging and were thrilled that it could predict movement,” said study author Dr. Sumner Norman.
To Dr. Krishna Shenoy at Stanford, who was not involved, the study will finally put ultrasound “on the map as a brain-machine interface technique. Adding to this toolkit is spectacular,” he said.
Breaking the Sound Barrier
Using sound to decode brain activity might seem preposterous, but ultrasound has had quite the run in medicine. You’ve probably heard of its most common use: taking photos of a fetus in pregnancy. The technique uses a transducer, which emits ultrasound pulses into the body and finds boundaries in tissue structure by analyzing the sound waves that bounce back.
Roughly a decade ago, neuroscientists realized they could adapt the tech for brain scanning. Rather than directly measuring the brain’s electrical chatter, it looks at a proxy—blood flow. When certain brain regions or circuits are active, the brain requires much more energy, which is provided by increased blood flow. In this way, functional ultrasound works similarly to functional MRI, but at a far higher resolution—roughly ten times, the authors said. Plus, people don’t have to lie very still in an expensive, claustrophobic magnet.
“A key question in this work was: If we have a technique like functional ultrasound that gives us high-resolution images of the brain’s blood flow dynamics in space and over time, is there enough information from that imaging to decode something useful about behavior?” said study author Dr. Mikhail Shapiro.
There’s plenty of reasons for doubt. As the new kid on the block, functional ultrasound has some known drawbacks. A major one: it gives a far less direct signal than electrodes. Previous studies show that, with multiple measurements, it can provide a rough picture of brain activity. But is that enough detail to guide a robotic prosthesis?
One-Trial Wonder
The new study put functional ultrasound to the ultimate test: could it reliably detect movement intention in monkeys? Because their brains are the most similar to ours, rhesus macaque monkeys are often the critical step before a brain-machine interface technology is adapted for humans.
The team first inserted small ultrasound transducers into the skulls of two rhesus monkeys. While it sounds intense, the surgery doesn’t penetrate the brain or its protective membrane; it’s only on the skull. Compared to electrodes, this means the brain itself isn’t physically harmed.
The device is linked to a computer, which controls the direction of sound waves and captures signals from the brain. For this study, the team aimed the pulses at the posterior parietal cortex, a part of the “motor” aspect of the brain, which plans movement. If right now you’re thinking about scrolling down this page, that’s the brain region already activated, before your fingers actually perform the movement.
Then came the tests. The first looked at eye movements—something pretty necessary before planning actual body movements without tripping all over the place. Here, the monkeys learned to focus on a central dot on a computer screen. A second dot, either left or right, then flashed. The monkeys’ task was to flicker their eyes to the most recent dot. It’s something that seems easy for us, but requires sophisticated brain computation.
The second task was more straightforward. Rather than just moving their eyes to the second target dot, the monkeys learned to grab and manipulate a joystick to move a cursor to that target.
Using brain imaging to decode the mind and control movement. Image Credit: S. Norman, Caltech
As the monkeys learned, so did the device. Ultrasound data capturing brain activity was fed into a sophisticated machine learning algorithm to guess the monkeys’ intentions. Here’s the kicker: once trained, using data from just a single trial, the algorithm was able to correctly predict the monkeys’ actual eye movement—whether left or right—with roughly 78 percent accuracy. The accuracy for correctly maneuvering the joystick was even higher, at nearly 90 percent.
That’s crazy accurate, and very much needed for a mind-controlled prosthetic. If you’re using a mind-controlled cursor or limb, the last thing you’d want is to have to imagine the movement multiple times before you actually click the web button, grab the door handle, or move your robotic leg.
Even more impressive is the resolution. Sound waves seem omnipresent, but with focused ultrasound, it’s possible to measure brain activity at a resolution of 100 microns—roughly 10 neurons in the brain.
A Cyborg Future?
Before you start worrying about scientists blasting your brain with sound waves to hack your mind, don’t worry. The new tech still requires skull surgery, meaning that a small chunk of skull needs to be removed. However, the brain itself is spared. This means that compared to electrodes, ultrasound could offer less damage and potentially a far longer mind reading than anything currently possible.
There are downsides. Focused ultrasound is far younger than any electrode-based neural implants, and can’t yet reliably decode 360-degree movement or fine finger movements. For now, the tech requires a wire to link the device to a computer, which is off-putting to many people and will prevent widespread adoption. Add to that the inherent downside of focused ultrasound, which lags behind electrical recordings by roughly two seconds.
All that aside, however, the tech is just tiptoeing into a future where minds and machines seamlessly connect. Ultrasound can penetrate the skull, though not yet at the resolution needed for imaging and decoding brain activity. The team is already working with human volunteers with traumatic brain injuries, who had to have a piece of their skulls removed, to see how well ultrasound works for reading their minds.
“What’s most exciting is that functional ultrasound is a young technique with huge potential. This is just our first step in bringing high performance, less invasive brain-machine interface to more people,” said Norman.
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#438078 How modern robots are developed
Today, neuroscience and robotics are developing hand in hand. Mikhail Lebedev, Academic Supervisor at HSE University's Centre for Bioelectric Interfaces, spoke about how studying the brain inspires the development of robots. Continue reading
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#437978 How Mirroring the Architecture of the ...
While AI can carry out some impressive feats when trained on millions of data points, the human brain can often learn from a tiny number of examples. New research shows that borrowing architectural principles from the brain can help AI get closer to our visual prowess.
The prevailing wisdom in deep learning research is that the more data you throw at an algorithm, the better it will learn. And in the era of Big Data, that’s easier than ever, particularly for the large data-centric tech companies carrying out a lot of the cutting-edge AI research.
Today’s largest deep learning models, like OpenAI’s GPT-3 and Google’s BERT, are trained on billions of data points, and even more modest models require large amounts of data. Collecting these datasets and investing the computational resources to crunch through them is a major bottleneck, particularly for less well-resourced academic labs.
It also means today’s AI is far less flexible than natural intelligence. While a human only needs to see a handful of examples of an animal, a tool, or some other category of object to be able pick it out again, most AI need to be trained on many examples of an object in order to be able to recognize it.
There is an active sub-discipline of AI research aimed at what is known as “one-shot” or “few-shot” learning, where algorithms are designed to be able to learn from very few examples. But these approaches are still largely experimental, and they can’t come close to matching the fastest learner we know—the human brain.
This prompted a pair of neuroscientists to see if they could design an AI that could learn from few data points by borrowing principles from how we think the brain solves this problem. In a paper in Frontiers in Computational Neuroscience, they explained that the approach significantly boosts AI’s ability to learn new visual concepts from few examples.
“Our model provides a biologically plausible way for artificial neural networks to learn new visual concepts from a small number of examples,” Maximilian Riesenhuber, from Georgetown University Medical Center, said in a press release. “We can get computers to learn much better from few examples by leveraging prior learning in a way that we think mirrors what the brain is doing.”
Several decades of neuroscience research suggest that the brain’s ability to learn so quickly depends on its ability to use prior knowledge to understand new concepts based on little data. When it comes to visual understanding, this can rely on similarities of shape, structure, or color, but the brain can also leverage abstract visual concepts thought to be encoded in a brain region called the anterior temporal lobe (ATL).
“It is like saying that a platypus looks a bit like a duck, a beaver, and a sea otter,” said paper co-author Joshua Rule, from the University of California Berkeley.
The researchers decided to try and recreate this capability by using similar high-level concepts learned by an AI to help it quickly learn previously unseen categories of images.
Deep learning algorithms work by getting layers of artificial neurons to learn increasingly complex features of an image or other data type, which are then used to categorize new data. For instance, early layers will look for simple features like edges, while later ones might look for more complex ones like noses, faces, or even more high-level characteristics.
First they trained the AI on 2.5 million images across 2,000 different categories from the popular ImageNet dataset. They then extracted features from various layers of the network, including the very last layer before the output layer. They refer to these as “conceptual features” because they are the highest-level features learned, and most similar to the abstract concepts that might be encoded in the ATL.
They then used these different sets of features to train the AI to learn new concepts based on 2, 4, 8, 16, 32, 64, and 128 examples. They found that the AI that used the conceptual features yielded much better performance than ones trained using lower-level features on lower numbers of examples, but the gap shrunk as they were fed more training examples.
While the researchers admit the challenge they set their AI was relatively simple and only covers one aspect of the complex process of visual reasoning, they said that using a biologically plausible approach to solving the few-shot problem opens up promising new avenues in both neuroscience and AI.
“Our findings not only suggest techniques that could help computers learn more quickly and efficiently, they can also lead to improved neuroscience experiments aimed at understanding how people learn so quickly, which is not yet well understood,” Riesenhuber said.
As the researchers note, the human visual system is still the gold standard when it comes to understanding the world around us. Borrowing from its design principles might turn out to be a profitable direction for future research.
Image Credit: Gerd Altmann from Pixabay Continue reading
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#437293 These Scientists Just Completed a 3D ...
Human brain maps are a dime a dozen these days. Maps that detail neurons in a certain region. Maps that draw out functional connections between those cells. Maps that dive deeper into gene expression. Or even meta-maps that combine all of the above.
But have you ever wondered: how well do those maps represent my brain? After all, no two brains are alike. And if we’re ever going to reverse-engineer the brain as a computer simulation—as Europe’s Human Brain Project is trying to do—shouldn’t we ask whose brain they’re hoping to simulate?
Enter a new kind of map: the Julich-Brain, a probabilistic map of human brains that accounts for individual differences using a computational framework. Rather than generating a static PDF of a brain map, the Julich-Brain atlas is also dynamic, in that it continuously changes to incorporate more recent brain mapping results. So far, the map has data from over 24,000 thinly sliced sections from 23 postmortem brains covering most years of adulthood at the cellular level. But the atlas can also continuously adapt to progress in mapping technologies to aid brain modeling and simulation, and link to other atlases and alternatives.
In other words, rather than “just another” human brain map, the Julich-Brain atlas is its own neuromapping API—one that could unite previous brain-mapping efforts with more modern methods.
“It is exciting to see how far the combination of brain research and digital technologies has progressed,” said Dr. Katrin Amunts of the Institute of Neuroscience and Medicine at Research Centre Jülich in Germany, who spearheaded the study.
The Old Dogma
The Julich-Brain atlas embraces traditional brain-mapping while also yanking the field into the 21st century.
First, the new atlas includes the brain’s cytoarchitecture, or how brain cells are organized. As brain maps go, these kinds of maps are the oldest and most fundamental. Rather than exploring how neurons talk to each other functionally—which is all the rage these days with connectome maps—cytoarchitecture maps draw out the physical arrangement of neurons.
Like a census, these maps literally capture how neurons are distributed in the brain, what they look like, and how they layer within and between different brain regions.
Because neurons aren’t packed together the same way between different brain regions, this provides a way to parse the brain into areas that can be further studied. When we say the brain’s “memory center,” the hippocampus, or the emotion center, the “amygdala,” these distinctions are based on cytoarchitectural maps.
Some may call this type of mapping “boring.” But cytoarchitecture maps form the very basis of any sort of neuroscience understanding. Like hand-drawn maps from early explorers sailing to the western hemisphere, these maps provide the brain’s geographical patterns from which we try to decipher functional connections. If brain regions are cities, then cytoarchitecture maps attempt to show trading or other “functional” activities that occur in the interlinking highways.
You might’ve heard of the most common cytoarchitecture map used today: the Brodmann map from 1909 (yup, that old), which divided the brain into classical regions based on the cells’ morphology and location. The map, while impactful, wasn’t able to account for brain differences between people. More recent brain-mapping technologies have allowed us to dig deeper into neuronal differences and divide the brain into more regions—180 areas in the cortex alone, compared with 43 in the original Brodmann map.
The new study took inspiration from that age-old map and transformed it into a digital ecosystem.
A Living Atlas
Work began on the Julich-Brain atlas in the mid-1990s, with a little help from the crowd.
The preparation of human tissue and its microstructural mapping, analysis, and data processing is incredibly labor-intensive, the authors lamented, making it impossible to do for the whole brain at high resolution in just one lab. To build their “Google Earth” for the brain, the team hooked up with EBRAINS, a shared computing platform set up by the Human Brain Project to promote collaboration between neuroscience labs in the EU.
First, the team acquired MRI scans of 23 postmortem brains, sliced the brains into wafer-thin sections, and scanned and digitized them. They corrected distortions from the chopping using data from the MRI scans and then lined up neurons in consecutive sections—picture putting together a 3D puzzle—to reconstruct the whole brain. Overall, the team had to analyze 24,000 brain sections, which prompted them to build a computational management system for individual brain sections—a win, because they could now track individual donor brains too.
Their method was quite clever. They first mapped their results to a brain template from a single person, called the MNI-Colin27 template. Because the reference brain was extremely detailed, this allowed the team to better figure out the location of brain cells and regions in a particular anatomical space.
However, MNI-Colin27’s brain isn’t your or my brain—or any of the brains the team analyzed. To dilute any of Colin’s potential brain quirks, the team also mapped their dataset onto an “average brain,” dubbed the ICBM2009c (catchy, I know).
This step allowed the team to “standardize” their results with everything else from the Human Connectome Project and the UK Biobank, kind of like adding their Google Maps layer to the existing map. To highlight individual brain differences, the team overlaid their dataset on existing ones, and looked for differences in the cytoarchitecture.
The microscopic architecture of neurons change between two areas (dotted line), forming the basis of different identifiable brain regions. To account for individual differences, the team also calculated a probability map (right hemisphere). Image credit: Forschungszentrum Juelich / Katrin Amunts
Based on structure alone, the brains were both remarkably different and shockingly similar at the same time. For example, the cortexes—the outermost layer of the brain—were physically different across donor brains of different age and sex. The region especially divergent between people was Broca’s region, which is traditionally linked to speech production. In contrast, parts of the visual cortex were almost identical between the brains.
The Brain-Mapping Future
Rather than relying on the brain’s visible “landmarks,” which can still differ between people, the probabilistic map is far more precise, the authors said.
What’s more, the map could also pool yet unmapped regions in the cortex—about 30 percent or so—into “gap maps,” providing neuroscientists with a better idea of what still needs to be understood.
“New maps are continuously replacing gap maps with progress in mapping while the process is captured and documented … Consequently, the atlas is not static but rather represents a ‘living map,’” the authors said.
Thanks to its structurally-sound architecture down to individual cells, the atlas can contribute to brain modeling and simulation down the line—especially for personalized brain models for neurological disorders such as seizures. Researchers can also use the framework for other species, and they can even incorporate new data-crunching processors into the workflow, such as mapping brain regions using artificial intelligence.
Fundamentally, the goal is to build shared resources to better understand the brain. “[These atlases] help us—and more and more researchers worldwide—to better understand the complex organization of the brain and to jointly uncover how things are connected,” the authors said.
Image credit: Richard Watts, PhD, University of Vermont and Fair Neuroimaging Lab, Oregon Health and Science University Continue reading
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#437202 Scientists Used Dopamine to Seamlessly ...
In just half a decade, neuromorphic devices—or brain-inspired computing—already seem quaint. The current darling? Artificial-biological hybrid computing, uniting both man-made computer chips and biological neurons seamlessly into semi-living circuits.
It sounds crazy, but a new study in Nature Materials shows that it’s possible to get an artificial neuron to communicate directly with a biological one using not just electricity, but dopamine—a chemical the brain naturally uses to change how neural circuits behave, most known for signaling reward.
Because these chemicals, known as “neurotransmitters,” are how biological neurons functionally link up in the brain, the study is a dramatic demonstration that it’s possible to connect artificial components with biological brain cells into a functional circuit.
The team isn’t the first to pursue hybrid neural circuits. Previously, a different team hooked up two silicon-based artificial neurons with a biological one into a circuit using electrical protocols alone. Although a powerful demonstration of hybrid computing, the study relied on only one-half of the brain’s computational ability: electrical computing.
The new study now tackles the other half: chemical computing. It adds a layer of compatibility that lays the groundwork not just for brain-inspired computers, but also for brain-machine interfaces and—perhaps—a sort of “cyborg” future. After all, if your brain can’t tell the difference between an artificial neuron and your own, could you? And even if you did, would you care?
Of course, that scenario is far in the future—if ever. For now, the team, led by Dr. Alberto Salleo, professor of materials science and engineering at Stanford University, collectively breathed a sigh of relief that the hybrid circuit worked.
“It’s a demonstration that this communication melding chemistry and electricity is possible,” said Salleo. “You could say it’s a first step toward a brain-machine interface, but it’s a tiny, tiny very first step.”
Neuromorphic Computing
The study grew from years of work into neuromorphic computing, or data processing inspired by the brain.
The blue-sky idea was inspired by the brain’s massive parallel computing capabilities, along with vast energy savings. By mimicking these properties, scientists reasoned, we could potentially turbo-charge computing. Neuromorphic devices basically embody artificial neural networks in physical form—wouldn’t hardware that mimics how the brain processes information be even more efficient and powerful?
These explorations led to novel neuromorphic chips, or artificial neurons that “fire” like biological ones. Additional work found that it’s possible to link these chips up into powerful circuits that run deep learning with ease, with bioengineered communication nodes called artificial synapses.
As a potential computing hardware replacement, these systems have proven to be incredibly promising. Yet scientists soon wondered: given their similarity to biological brains, can we use them as “replacement parts” for brains that suffer from traumatic injuries, aging, or degeneration? Can we hook up neuromorphic components to the brain to restore its capabilities?
Buzz & Chemistry
Theoretically, the answer’s yes.
But there’s a huge problem: current brain-machine interfaces only use electrical signals to mimic neural computation. The brain, in contrast, has two tricks up its sleeve: electricity and chemicals, or electrochemical.
Within a neuron, electricity travels up its incoming branches, through the bulbous body, then down the output branches. When electrical signals reach the neuron’s outgoing “piers,” dotted along the output branch, however, they hit a snag. A small gap exists between neurons, so to get to the other side, the electrical signals generally need to be converted into little bubble ships, packed with chemicals, and set sail to the other neuronal shore.
In other words, without chemical signals, the brain can’t function normally. These neurotransmitters don’t just passively carry information. Dopamine, for example, can dramatically change how a neural circuit functions. For an artificial-biological hybrid neural system, the absence of chemistry is like nixing international cargo vessels and only sticking with land-based trains and highways.
“To emulate biological synaptic behavior, the connectivity of the neuromorphic device must be dynamically regulated by the local neurotransmitter activity,” the team said.
Let’s Get Electro-Chemical
The new study started with two neurons: the upstream, an immortalized biological cell that releases dopamine; and the downstream, an artificial neuron that the team previously introduced in 2017, made of a mix of biocompatible and electrical-conducting materials.
Rather than the classic neuron shape, picture more of a sandwich with a chunk bitten out in the middle (yup, I’m totally serious). Each of the remaining parts of the sandwich is a soft electrode, made of biological polymers. The “bitten out” part has a conductive solution that can pass on electrical signals.
The biological cell sits close to the first electrode. When activated, it dumps out boats of dopamine, which drift to the electrode and chemically react with it—mimicking the process of dopamine docking onto a biological neuron. This, in turn, generates a current that’s passed on to the second electrode through the conductive solution channel. When this current reaches the second electrode, it changes the electrode’s conductance—that is, how well it can pass on electrical information. This second step is analogous to docked dopamine “ships” changing how likely it is that a biological neuron will fire in the future.
In other words, dopamine release from the biological neuron interacts with the artificial one, so that the chemicals change how the downstream neuron behaves in a somewhat lasting way—a loose mimic of what happens inside the brain during learning.
But that’s not all. Chemical signaling is especially powerful in the brain because it’s flexible. Dopamine, for example, only grabs onto the downstream neurons for a bit before it returns back to its upstream neuron—that is, recycled or destroyed. This means that its effect is temporary, giving the neural circuit breathing room to readjust its activity.
The Stanford team also tried reconstructing this quirk in their hybrid circuit. They crafted a microfluidic channel that shuttles both dopamine and its byproduct away from the artificial neurons after they’ve done their job for recycling.
Putting It All Together
After confirming that biological cells can survive happily on top of the artificial one, the team performed a few tests to see if the hybrid circuit could “learn.”
They used electrical methods to first activate the biological dopamine neuron, and watched the artificial one. Before the experiment, the team wasn’t quite sure what to expect. Theoretically, it made sense that dopamine would change the artificial neuron’s conductance, similar to learning. But “it was hard to know whether we’d achieve the outcome we predicted on paper until we saw it happen in the lab,” said study author Scott Keene.
On the first try, however, the team found that the burst of chemical signaling was able to change the artificial neuron’s conductance long-term, similar to the neuroscience dogma “neurons that fire together, wire together.” Activating the upstream biological neuron with chemicals also changed the artificial neuron’s conductance in a way that mimicked learning.
“That’s when we realized the potential this has for emulating the long-term learning process of a synapse,” said Keene.
Visualizing under an electron microscope, the team found that, similar to its biological counterpart, the hybrid synapse was able to efficiently recycle dopamine with timescales similar to the brain after some calibration. By playing with how much dopamine accumulates at the artificial neuron, the team found that they loosely mimic a learning rule called spike learning—a darling of machine learning inspired by the brain’s computation.
A Hybrid Future?
Unfortunately for cyborg enthusiasts, the work is still in its infancy.
For one, the artificial neurons are still rather bulky compared to biological ones. This means that they can’t capture and translate information from a single “boat” of dopamine. It’s also unclear if, and how, a hybrid synapse can work inside a living brain. Given the billions of synapses firing away in our heads, it’ll be a challenge to find-and-replace those that need replacement, and be able to control our memories and behaviors similar to natural ones.
That said, we’re inching ever closer to full-capability artificial-biological hybrid circuits.
“The neurotransmitter-mediated neuromorphic device presented in this work constitutes a fundamental building block for artificial neural networks that can be directly modulated based on biological feedback from live neurons,” the authors concluded. “[It] is a crucial first step in realizing next-generation adaptive biohybrid interfaces.”
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