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Ask any neuroscientist to draw you a neuron, and it’ll probably look something like a star with two tails: one stubby with extensive tree-like branches, the other willowy, lengthy and dotted with spindly spikes.
While a decent abstraction, this cartoonish image hides the uncomfortable truth that scientists still don’t know much about what many neurons actually look like, not to mention the extent of their connections.
But without untangling the jumbled mess of neural wires that zigzag across the brain, scientists are stumped in trying to answer one of the most fundamental mysteries of the brain: how individual neuronal threads carry and assemble information, which forms the basis of our thoughts, memories, consciousness, and self.
What if there was a way to virtually trace and explore the brain’s serpentine fibers, much like the way Google Maps allows us to navigate the concrete tangles of our cities’ highways?
Thanks to an interdisciplinary team at Janelia Research Campus, we’re on our way. Meet MouseLight, the most extensive map of the mouse brain ever attempted. The ongoing project has an ambitious goal: reconstructing thousands—if not more—of the mouse’s 70 million neurons into a 3D map. (You can play with it here!)
With map in hand, neuroscientists around the world can begin to answer how neural circuits are organized in the brain, and how information flows from one neuron to another across brain regions and hemispheres.
The first release, presented Monday at the Society for Neuroscience Annual Conference in Washington, DC, contains information about the shape and sizes of 300 neurons.
And that’s just the beginning.
“MouseLight’s new dataset is the largest of its kind,” says Dr. Wyatt Korff, director of project teams. “It’s going to change the textbook view of neurons.”
MouseLight is hardly the first rodent brain atlasing project.
The Mouse Brain Connectivity Atlas at the Allen Institute for Brain Science in Seattle tracks neuron activity across small circuits in an effort to trace a mouse’s connectome—a complete atlas of how the firing of one neuron links to the next.
MICrONS (Machine Intelligence from Cortical Networks), the $100 million government-funded “moonshot” hopes to distill brain computation into algorithms for more powerful artificial intelligence. Its first step? Brain mapping.
What makes MouseLight stand out is its scope and level of detail.
MICrONS, for example, is focused on dissecting a cubic millimeter of the mouse visual processing center. In contrast, MouseLight involves tracing individual neurons across the entire brain.
And while connectomics outlines the major connections between brain regions, the birds-eye view entirely misses the intricacies of each individual neuron. This is where MouseLight steps in.
Slice and Dice
With a width only a fraction of a human hair, neuron projections are hard to capture in their native state. Tug or squeeze the brain too hard, and the long, delicate branches distort or even shred into bits.
In fact, previous attempts at trying to reconstruct neurons at this level of detail topped out at just a dozen, stymied by technological hiccups and sky-high costs.
A few years ago, the MouseLight team set out to automate the entire process, with a few time-saving tweaks. Here’s how it works.
After injecting a mouse with a virus that causes a handful of neurons to produce a green-glowing protein, the team treated the brain with a sugar alcohol solution. This step “clears” the brain, transforming the beige-colored organ to translucent, making it easier for light to penetrate and boosting the signal-to-background noise ratio. The brain is then glued onto a small pedestal and ready for imaging.
Building upon an established method called “two-photon microscopy,” the team then tweaked several parameters to reduce imaging time from days (or weeks) down to a fraction of that. Endearingly known as “2P” by the experts, this type of laser microscope zaps the tissue with just enough photos to light up a single plane without damaging the tissue—sharper plane, better focus, crisper image.
After taking an image, the setup activates its vibrating razor and shaves off the imaged section of the brain—a waspy slice about 200 micrometers thick. The process is repeated until the whole brain is imaged.
This setup increased imaging speed by 16 to 48 times faster than conventional microscopy, writes team leader Dr. Jayaram Chandrashekar, who published a version of the method early last year in eLife.
The resulting images strikingly highlight every crook and cranny of a neuronal branch, popping out against a pitch-black background. But pretty pictures come at a hefty data cost: each image takes up a whopping 20 terabytes of data—roughly the storage space of 4,000 DVDs, or 10,000 hours of movies.
Stitching individual images back into 3D is an image-processing nightmare. The MouseLight team used a combination of computational power and human prowess to complete this final step.
The reconstructed images are handed off to a mighty team of seven trained neuron trackers. With the help of tracing algorithms developed in-house and a keen eye, each member can track roughly a neuron a day—significantly less time than the week or so previously needed.
A Numbers Game
Even with just 300 fully reconstructed neurons, MouseLight has already revealed new secrets of the brain.
While it’s widely accepted that axons, the neurons’ outgoing projection, can span the entire length of the brain, these extra-long connections were considered relatively rare. (In fact, one previously discovered “giant neuron” was thought to link to consciousness because of its expansive connections).
Images captured from two-photon microscopy show an axon and dendrites protruding from a neuron’s cell body (sphere in center). Image Credit: Janelia Research Center, MouseLight project team
MouseLight blows that theory out of the water.
The data clearly shows that “giant neurons” are far more common than previously thought. For example, four neurons normally associated with taste had wiry branches that stretched all the way into brain areas that control movement and process touch.
“We knew that different regions of the brain talked to each other, but seeing it in 3D is different,” says Dr. Eve Marder at Brandeis University.
“The results are so stunning because they give you a really clear view of how the whole brain is connected.”
With a tested and true system in place, the team is now aiming to add 700 neurons to their collection within a year.
But appearance is only part of the story.
We can’t tell everything about a person simply by how they look. Neurons are the same: scientists can only infer so much about a neuron’s function by looking at their shape and positions. The team also hopes to profile the gene expression patterns of each neuron, which could provide more hints to their roles in the brain.
MouseLight essentially dissects the neural infrastructure that allows information traffic to flow through the brain. These anatomical highways are just the foundation. Just like Google Maps, roads form only the critical first layer of the map. Street view, traffic information and other add-ons come later for a complete look at cities in flux.
The same will happen for understanding our ever-changing brain.
Image Credit: Janelia Research Campus, MouseLight project team Continue reading
Major websites all over the world use a system called CAPTCHA to verify that someone is indeed a human and not a bot when entering data or signing into an account. CAPTCHA stands for the “Completely Automated Public Turing test to tell Computers and Humans Apart.” The squiggly letters and numbers, often posted against photographs or textured backgrounds, have been a good way to foil hackers. They are annoying but effective.
The days of CAPTCHA as a viable line of defense may, however, be numbered.
Researchers at Vicarious, a Californian artificial intelligence firm funded by Amazon founder Jeff Bezos and Facebook’s Mark Zuckerberg, have just published a paper documenting how they were able to defeat CAPTCHA using new artificial intelligence techniques. Whereas today’s most advanced artificial intelligence (AI) technologies use neural networks that require massive amounts of data to learn from, sometimes millions of examples, the researchers said their system needed just five training steps to crack Google’s reCAPTCHA technology. With this, they achieved a 67 percent success rate per character—reasonably close to the human accuracy rate of 87 percent. In answering PayPal and Yahoo CAPTCHAs, the system achieved an accuracy rate of greater than 50 percent.
The CAPTCHA breakthrough came hard on the heels of another major milestone from Google’s DeepMind team, the people who built the world’s best Go-playing system. DeepMind built a new artificial-intelligence system called AlphaGo Zero that taught itself to play the game at a world-beating level with minimal training data, mainly using trial and error—in a fashion similar to how humans learn.
Both playing Go and deciphering CAPTCHAs are clear examples of what we call narrow AI, which is different from artificial general intelligence (AGI)—the stuff of science fiction. Remember R2-D2 of Star Wars, Ava from Ex Machina, and Samantha from Her? They could do many things and learned everything they needed on their own.
Narrow AI technologies are systems that can only perform one specific type of task. For example, if you asked AlphaGo Zero to learn to play Monopoly, it could not, even though that is a far less sophisticated game than Go. If you asked the CAPTCHA cracker to learn to understand a spoken phrase, it would not even know where to start.
To date, though, even narrow AI has been difficult to build and perfect. To perform very elementary tasks such as determining whether an image is of a cat or a dog, the system requires the development of a model that details exactly what is being analyzed and massive amounts of data with labeled examples of both. The examples are used to train the AI systems, which are modeled on the neural networks in the brain, in which the connections between layers of neurons are adjusted based on what is observed. To put it simply, you tell an AI system exactly what to learn, and the more data you give it, the more accurate it becomes.
The methods that Vicarious and Google used were different; they allowed the systems to learn on their own, albeit in a narrow field. By making their own assumptions about what the training model should be and trying different permutations until they got the right results, they were able to teach themselves how to read the letters in a CAPTCHA or to play a game.
This blurs the line between narrow AI and AGI and has broader implications in robotics and virtually any other field in which machine learning in complex environments may be relevant.
Beyond visual recognition, the Vicarious breakthrough and AlphaGo Zero success are encouraging scientists to think about how AIs can learn to do things from scratch. And this brings us one step closer to coexisting with classes of AIs and robots that can learn to perform new tasks that are slight variants on their previous tasks—and ultimately the AGI of science fiction.
So R2-D2 may be here sooner than we expected.
This article was originally published by The Washington Post. Read the original article here.
Image Credit: Zapp2Photo / Shutterstock.com Continue reading
For Dr. Hiroshi Ishiguro, one of the most interesting things about androids is the changing questions they pose us, their creators, as they evolve. Does it, for example, do something to the concept of being human if a human-made creation starts telling you about what kind of boys ‘she’ likes?
If you want to know the answer to the boys question, you need to ask ERICA, one of Dr. Ishiguro’s advanced androids. Beneath her plastic skull and silicone skin, wires connect to AI software systems that bring her to life. Her ability to respond goes far beyond standard inquiries. Spend a little time with her, and the feeling of a distinct personality starts to emerge. From time to time, she works as a receptionist at Dr. Ishiguro and his team’s Osaka University labs. One of her android sisters is an actor who has starred in plays and a film.
ERICA’s ‘brother’ is an android version of Dr. Ishiguro himself, which has represented its creator at various events while the biological Ishiguro can remain in his offices in Japan. Microphones and cameras capture Ishiguro’s voice and face movements, which are relayed to the android. Apart from mimicking its creator, the Geminoid™ android is also capable of lifelike blinking, fidgeting, and breathing movements.
Say hello to relaxation
As technological development continues to accelerate, so do the possibilities for androids. From a position as receptionist, ERICA may well branch out into many other professions in the coming years. Companion for the elderly, comic book storyteller (an ancient profession in Japan), pop star, conversational foreign language partner, and newscaster are some of the roles and responsibilities Dr. Ishiguro sees androids taking on in the near future.
“Androids are not uncanny anymore. Most people adapt to interacting with Erica very quickly. Actually, I think that in interacting with androids, which are still different from us, we get a better appreciation of interacting with other cultures. In both cases, we are talking with someone who is different from us and learn to overcome those differences,” he says.
A lot has been written about how robots will take our jobs. Dr. Ishiguro believes these fears are blown somewhat out of proportion.
“Robots and androids will take over many simple jobs. Initially there might be some job-related issues, but new schemes, like for example a robot tax similar to the one described by Bill Gates, should help,” he says.
“Androids will make it possible for humans to relax and keep evolving. If we compare the time we spend studying now compared to 100 years ago, it has grown a lot. I think it needs to keep growing if we are to keep expanding our scientific and technological knowledge. In the future, we might end up spending 20 percent of our lifetime on work and 80 percent of the time on education and growing our skills.”
Android asks who you are
For Dr. Ishiguro, another aspect of robotics in general, and androids in particular, is how they question what it means to be human.
“Identity is a very difficult concept for humans sometimes. For example, I think clothes are part of our identity, in a way that is similar to our faces and bodies. We don’t change those from one day to the next, and that is why I have ten matching black outfits,” he says.
This link between physical appearance and perceived identity is one of the aspects Dr. Ishiguro is exploring. Another closely linked concept is the connection between body and feeling of self. The Ishiguro avatar was once giving a presentation in Austria. Its creator recalls how he felt distinctly like he was in Austria, even capable of feeling sensation of touch on his own body when people laid their hands on the android. If he was distracted, he felt almost ‘sucked’ back into his body in Japan.
“I am constantly thinking about my life in this way, and I believe that androids are a unique mirror that helps us formulate questions about why we are here and why we have been so successful. I do not necessarily think I have found the answers to these questions, so if you have, please share,” he says with a laugh.
His work and these questions, while extremely interesting on their own, become extra poignant when considering the predicted melding of mind and machine in the near future.
The ability to be present in several locations through avatars—virtual or robotic—raises many questions of both philosophical and practical nature. Then add the hypotheticals, like why send a human out onto the hostile surface of Mars if you could send a remote-controlled android, capable of relaying everything it sees, hears and feels?
The two ways of robotics will meet
Dr. Ishiguro sees the world of AI-human interaction as currently roughly split into two. One is the chat-bot approach that companies like Amazon, Microsoft, Google, and recently Apple, employ using stationary objects like speakers. Androids like ERICA represent another approach.
“It is about more than the form factor. I think that the android approach is generally more story-based. We are integrating new conversation features based on assumptions about the situation and running different scenarios that expand the android’s vocabulary and interactions. Another aspect we are working on is giving androids desire and intention. Like with people, androids should have desires and intentions in order for you to want to interact with them over time,” Dr. Ishiguro explains.
This could be said to be part of a wider trend for Japan, where many companies are developing human-like robots that often have some Internet of Things capabilities, making them able to handle some of the same tasks as an Amazon Echo. The difference in approach could be summed up in the words ‘assistant’ (Apple, Amazon, etc.) and ‘companion’ (Japan).
Dr. Ishiguro sees this as partly linked to how Japanese as a language—and market—is somewhat limited. This has a direct impact on viability and practicality of ‘pure’ voice recognition systems. At the same time, Japanese people have had greater exposure to positive images of robots, and have a different cultural / religious view of objects having a ‘soul’. However, it may also mean Japanese companies and android scientists are both stealing a lap on their western counterparts.
“If you speak to an Amazon Echo, that is not a natural way to interact for humans. This is part of why we are making human-like robot systems. The human brain is set up to recognize and interact with humans. So, it makes sense to focus on developing the body for the AI mind, as well as the AI. I believe that the final goal for both Japanese and other companies and scientists is to create human-like interaction. Technology has to adapt to us, because we cannot adapt fast enough to it, as it develops so quickly,” he says.
Banner image courtesy of Hiroshi Ishiguro Laboratories, ATR all rights reserved.
Dr. Ishiguro’s team has collaborated with partners and developed a number of android systems:
Geminoid™ HI-2 has been developed by Hiroshi Ishiguro Laboratories and Advanced Telecommunications Research Institute International (ATR).
Geminoid™ F has been developed by Osaka University and Hiroshi Ishiguro Laboratories, Advanced Telecommunications Research Institute International (ATR).
ERICA has been developed by ERATO ISHIGURO Symbiotic Human-Robot Interaction Project Continue reading