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#439280 Google and Harvard Unveil the Largest ...
Last Tuesday, teams from Google and Harvard published an intricate map of every cell and connection in a cubic millimeter of the human brain.
The mapped region encompasses the various layers and cell types of the cerebral cortex, a region of brain tissue associated with higher-level cognition, such as thinking, planning, and language. According to Google, it’s the largest brain map at this level of detail to date, and it’s freely available to scientists (and the rest of us) online. (Really. Go here. Take a stroll.)
To make the map, the teams sliced donated tissue into 5,300 sections, each 30 nanometers thick, and imaged them with a scanning electron microscope at a resolution of 4 nanometers. The resulting 225 million images were computationally aligned and stitched back into a 3D digital representation of the region. Machine learning algorithms segmented individual cells and classified synapses, axons, dendrites, cells, and other structures, and humans checked their work. (The team posted a pre-print paper about the map on bioArxiv.)
Last year, Google and the Janelia Research Campus of the Howard Hughes Medical Institute made headlines when they similarly mapped a portion of a fruit fly brain. That map, at the time the largest yet, covered some 25,000 neurons and 20 million synapses. In addition to targeting the human brain, itself of note, the new map includes tens of thousands of neurons and 130 million synapses. It takes up 1.4 petabytes of disk space.
By comparison, over three decades’ worth of satellite images of Earth by NASA’s Landsat program require 1.3 petabytes of storage. Collections of brain images on the smallest scales are like “a world in a grain of sand,” the Allen Institute’s Clay Reid told Nature, quoting William Blake in reference to an earlier map of the mouse brain.
All that, however, is but a millionth of the human brain. Which is to say, a similarly detailed map of the entire thing is yet years away. Still, the work shows how fast the field is moving. A map of this scale and detail would have been unimaginable a few decades ago.
How to Map a Brain
The study of the brain’s cellular circuitry is known as connectomics.
Obtaining the human connectome, or the wiring diagram of a whole brain, is a moonshot akin to the human genome. And like the human genome, at first, it seemed an impossible feat.
The only complete connectomes are for simple creatures: the nematode worm (C. elegans) and the larva of a sea creature called C. intestinalis. There’s a very good reason for that. Until recently, the mapping process was time-consuming and costly.
Researchers mapping C. elegans in the 1980s used a film camera attached to an electron microscope to image slices of the worm, then reconstructed the neurons and synaptic connections by hand, like a maddeningly difficult three-dimensional puzzle. C. elegans has only 302 neurons and roughly 7,000 synapses, but the rough draft of its connectome took 15 years, and a final draft took another 20. Clearly, this approach wouldn’t scale.
What’s changed? In short, automation.
These days the images themselves are, of course, digital. A process known as focused ion beam milling shaves down each slice of tissue a few nanometers at a time. After one layer is vaporized, an electron microscope images the newly exposed layer. The imaged layer is then shaved away by the ion beam and the next one imaged, until all that’s left of the slice of tissue is a nanometer-resolution digital copy. It’s a far cry from the days of Kodachrome.
But maybe the most dramatic improvement is what happens after scientists complete that pile of images.
Instead of assembling them by hand, algorithms take over. Their first job is ordering the imaged slices. Then they do something impossible until the last decade. They line up the images just so, tracing the path of cells and synapses between them and thus building a 3D model. Humans still proofread the results, but they don’t do the hardest bit anymore. (Even the proofreading can be refined. Renowned neuroscientist and connectomics proponent Sebastian Seung, for example, created a game called Eyewire, where thousands of volunteers review structures.)
“It’s truly beautiful to look at,” Harvard’s Jeff Lichtman, whose lab collaborated with Google on the new map, told Nature in 2019. The programs can trace out neurons faster than the team can churn out image data, he said. “We’re not able to keep up with them. That’s a great place to be.”
But Why…?
In a 2010 TED talk, Seung told the audience you are your connectome. Reconstruct the connections and you reconstruct the mind itself: memories, experience, and personality.
But connectomics has not been without controversy over the years.
Not everyone believes mapping the connectome at this level of detail is necessary for a deep understanding of the brain. And, especially in the field’s earlier, more artisanal past, researchers worried the scale of resources required simply wouldn’t yield comparably valuable (or timely) results.
“I don’t need to know the precise details of the wiring of each cell and each synapse in each of those brains,” nueroscientist Anthony Movshon said in 2019. “What I need to know, instead, is the organizational principles that wire them together.” These, Movshon believes, can likely be inferred from observations at lower resolutions.
Also, a static snapshot of the brain’s physical connections doesn’t necessarily explain how those connections are used in practice.
“A connectome is necessary, but not sufficient,” some scientists have said over the years. Indeed, it may be in the combination of brain maps—including functional, higher-level maps that track signals flowing through neural networks in response to stimuli—that the brain’s inner workings will be illuminated in the sharpest detail.
Still, the C. elegans connectome has proven to be a foundational building block for neuroscience over the years. And the growing speed of mapping is beginning to suggest goals that once seemed impractical may actually be within reach in the coming decades.
Are We There Yet?
Seung has said that when he first started out he estimated it’d take a million years for a person to manually trace all the connections in a cubic millimeter of human cortex. The whole brain, he further inferred, would take on the order of a trillion years.
That’s why automation and algorithms have been so crucial to the field.
Janelia’s Gerry Rubin told Stat he and his team have overseen a 1,000-fold increase in mapping speed since they began work on the fruit fly connectome in 2008. The full connectome—the first part of which was completed last year—may arrive in 2022.
Other groups are working on other animals, like octopuses, saying comparing how different forms of intelligence are wired up may prove particularly rich ground for discovery.
The full connectome of a mouse, a project already underway, may follow the fruit fly by the end of the decade. Rubin estimates going from mouse to human would need another million-fold jump in mapping speed. But he points to the trillion-fold increase in DNA sequencing speed since 1973 to show such dramatic technical improvements aren’t unprecedented.
The genome may be an apt comparison in another way too. Even after sequencing the first human genome, it’s taken many years to scale genomics to the point we can more fully realize its potential. Perhaps the same will be true of connectomics.
Even as the technology opens new doors, it may take time to understand and make use of all it has to offer.
“I believe people were impatient about what [connectomes] would provide,” Joshua Vogelstein, cofounder of the Open Connetome Project, told the Verge last year. “The amount of time between a good technology being seeded, and doing actual science using that technology is often approximately 15 years. Now it’s 15 years later and we can start doing science.”
Proponents hope brain maps will yield new insights into how the brain works—from thinking to emotion and memory—and how to better diagnose and treat brain disorders. Others, Google among them no doubt, hope to glean insights that could lead to more efficient computing (the brain is astonishing in this respect) and powerful artificial intelligence.
There’s no telling exactly what scientists will find as, neuron by synapse, they map the inner workings of our minds—but it seems all but certain great discoveries await.
Image Credit: Google / Harvard Continue reading
#438014 Meet Blueswarm, a Smart School of ...
Anyone who’s seen an undersea nature documentary has marveled at the complex choreography that schooling fish display, a darting, synchronized ballet with a cast of thousands.
Those instinctive movements have inspired researchers at the Harvard John A. Paulson School of Engineering and Applied Sciences (SEAS), and the Wyss Institute for Biologically Inspired Engineering. The results could improve the performance and dependability of not just underwater robots, but other vehicles that require decentralized locomotion and organization, such as self-driving cars and robotic space exploration.
The fish collective called Blueswarm was created by a team led by Radhika Nagpal, whose lab is a pioneer in self-organizing systems. The oddly adorable robots can sync their movements like biological fish, taking cues from their plastic-bodied neighbors with no external controls required. Nagpal told IEEE Spectrum that this marks a milestone, demonstrating complex 3D behaviors with implicit coordination in underwater robots.
“Insights from this research will help us develop future miniature underwater swarms that can perform environmental monitoring and search in visually-rich but fragile environments like coral reefs,” Nagpal said. “This research also paves a way to better understand fish schools, by synthetically recreating their behavior.”
The research is published in Science Robotics, with Florian Berlinger as first author. Berlinger said the “Bluedot” robots integrate a trio of blue LED lights, a lithium-polymer battery, a pair of cameras, a Raspberry Pi computer and four controllable fins within a 3D-printed hull. The fish-lens cameras detect LED’s of their fellow swimmers, and apply a custom algorithm to calculate distance, direction and heading.
Based on that simple production and detection of LED light, the team proved that Blueswarm could self-organize behaviors, including aggregation, dispersal and circle formation—basically, swimming in a clockwise synchronization. Researchers also simulated a successful search mission, an autonomous Finding Nemo. Using their dispersion algorithm, the robot school spread out until one could detect a red light in the tank. Its blue LEDs then flashed, triggering the aggregation algorithm to gather the school around it. Such a robot swarm might prove valuable in search-and-rescue missions at sea, covering miles of open water and reporting back to its mates.
“Each Bluebot implicitly reacts to its neighbors’ positions,” Berlinger said. The fish—RoboCod, perhaps?—also integrate a Wifi module to allow uploading new behaviors remotely. The lab’s previous efforts include a 1,000-strong army of “Kilobots,” and a robotic construction crew inspired by termites. Both projects operated in two-dimensional space. But a 3D environment like air or water posed a tougher challenge for sensing and movement.
In nature, Berlinger notes, there’s no scaly CEO to direct the school’s movements. Nor do fish communicate their intentions. Instead, so-called “implicit coordination” guides the school’s collective behavior, with individual members executing high-speed moves based on what they see their neighbors doing. That decentralized, autonomous organization has long fascinated scientists, including in robotics.
“In these situations, it really benefits you to have a highly autonomous robot swarm that is self-sufficient. By using implicit rules and 3D visual perception, we were able to create a system with a high degree of autonomy and flexibility underwater where things like GPS and WiFi are not accessible.”
Berlinger adds the research could one day translate to anything that requires decentralized robots, from self-driving cars and Amazon warehouse vehicles to exploration of faraway planets, where poor latency makes it impossible to transmit commands quickly. Today’s semi-autonomous cars face their own technical hurdles in reliably sensing and responding to their complex environments, including when foul weather obscures onboard sensors or road markers, or when they can’t fix position via GPS. An entire subset of autonomous-car research involves vehicle-to-vehicle (V2V) communications that could give cars a hive mind to guide individual or collective decisions— avoiding snarled traffic, driving safely in tight convoys, or taking group evasive action during a crash that’s beyond their sensory range.
“Once we have millions of cars on the road, there can’t be one computer orchestrating all the traffic, making decisions that work for all the cars,” Berlinger said.
The miniature robots could also work long hours in places that are inaccessible to humans and divers, or even large tethered robots. Nagpal said the synthetic swimmers could monitor and collect data on reefs or underwater infrastructure 24/7, and work into tiny places without disturbing fragile equipment or ecosystems.
“If we could be as good as fish in that environment, we could collect information and be non-invasive, in cluttered environments where everything is an obstacle,” Nagpal said. Continue reading