Posts tagged Eyewire
Articles and news from the latest research reports.
Wiring of retina reveals how eyes sense motion
Online gamers helped researchers map neuron connections involved in detecting direction of moving objects.
A vast project to map neural connections in the mouse retina may have answered the long-standing question of how the eyes detect motion. With the help of volunteers who played an online brain-mapping game, researchers showed that pairs of neurons positioned along a given direction together cause a third neuron to fire in response to images moving in the same direction.
It is sometimes said that we see with the brain rather than the eyes, but this is not entirely true. People can only make sense of visual information once it has been interpreted by the brain, but some of this information is processed partly by neurons in the retina. In particular, 50 years ago researchers discovered that the mammalian retina is sensitive to the direction and speed of moving images. This showed that motion perception begins in the retina, but researchers struggled to explain how.
Wiring diagram of retinal neurons is first step toward mapping the human brain.
The human brain has 100 billion neurons, connected to each other in networks that allow us to interpret the world around us, plan for the future, and control our actions and movements. MIT neuroscientist Sebastian Seung wants to map those networks, creating a wiring diagram of the brain that could help scientists learn how we each become our unique selves.
In a paper appearing in the Aug. 7 online edition of Nature, Seung and collaborators at MIT and the Max Planck Institute for Medical Research in Germany have reported their first step toward this goal: Using a combination of human and artificial intelligence, they have mapped all the wiring among 950 neurons within a tiny patch of the mouse retina.
Composed of neurons that process visual information, the retina is technically part of the brain and is a more approachable starting point, Seung says. By mapping all of the neurons in this 117-micrometer-by-80-micrometer patch of tissue, the researchers were able to classify most of the neurons they found, based on their patterns of wiring. They also identified a new type of retinal cell that had not been seen before.
“It’s the complete reconstruction of all the neurons inside this patch. No one’s ever done that before in the mammalian nervous system,” says Seung, a professor of computational neuroscience at MIT.
Other MIT authors of the paper are former postdoc Srinivas Turaga and former graduate student Viren Jain. The Max Planck team was led by Winfried Denk, a physicist and the Max Planck Institute’s director. Moritz Helmstaedter, a research group leader at the Max Planck Institute, is the lead author of the paper, and Kevin Briggman, a former postdoc at Max Planck, is also an author.
Tracing connections
Neurons in the retina are classified into five classes: photoreceptors, horizontal cells, bipolar cells, amacrine cells and ganglion cells. Within each class are many types, classified by shape and by the connections they make with other neurons.
“Neurons come in many types, and the retina is estimated to contain 50 to 100 types, but they’ve never been exhaustively characterized. And their connections are even less well known,” Seung says.
In this study, the research team focused on a section of the retina known as the inner plexiform layer, which is one of several layers sandwiched between the photoreceptors, which receive visual input, and the ganglion cells, which relay visual information to the brain via the optic nerve. The neurons of the inner plexiform layer help to process visual information as it passes from the surface of the eye to the optic nerve.
To map all of the connections in this small patch of retina, the researchers first took electron micrographs of the targeted section. The Max Planck researchers obtained these images using a technique called serial block face scanning electron microscopy, which they invented to generate high-resolution three-dimensional images of biological samples.
Developing a wiring diagram from these images required both human and artificial intelligence. First, the researchers hired about 225 German undergraduates to trace the “skeleton” of each neuron, which took more than 20,000 hours of work (a little more than two years).
To flesh out the bodies of the neurons, the researchers fed these traced skeletons into a computer algorithm developed in Seung’s lab, which expands the skeletons into full neuron shapes. The researchers used machine learning to train the algorithm, known as a convolutional network, to detect the boundaries between neurons. Using those as reference points, the algorithm can fill in the entire body of each neuron.
“Tracing neurons in these images is probably one of the world’s most challenging computer vision problems. Our convolutional networks are actually deep artificial neural networks designed with inspiration from how our own visual system processes visual information to solve these difficult problems,” Turaga says.
If human workers were to fill in the entire neuron body, it would take 10 to 100 times longer than just drawing the skeleton. “This speeds up the whole process,” Seung says. “It’s a way of combining human and machine intelligence.”
The only previous complete wiring diagram, which mapped all of the connections between the 302 neurons found in the worm Caenorhabditis elegans, was reported in 1986 and required more than a dozen years of tedious labor.
“I think this is going to be a really significant paper in the history of how we study complex systems,” says Richard Masland, a professor of ophthalmology at the Massachusetts Eye and Ear Infirmary, who was not part of the research team. “This paper identifies circuit motifs that are interesting but really are just symbolic of the many types of questions that could be answered using these techniques.”
Classifying neurons
Wiring diagrams allow scientists to see where neurons connect with each other to form synapses — the junctions that allow neurons to relay messages. By analyzing how neurons are connected to each other, researchers can classify different types of neurons.
The researchers were able to identify most of the 950 neurons included in the new retinal-wiring diagram based on their connections with other neurons, as well as the shape of the neuron. A handful of neurons could not be classified because there was only one of their type, or because only a fragment of the neuron was included in the imaged sample.
“We haven’t completed the project of classifying types but this shows that it should be possible. This method should be able to do it, in principle, if it’s scaled up to a larger piece of tissue,” Seung says.
In this study, the researchers identified a new class of bipolar cells, which relay information from photoreceptors to ganglion cells. However, further study is needed to determine this cell type’s exact function.
Seung’s lab is now working on a wiring diagram of a larger piece of the retina — 0.3 millimeter by 0.3 millimeter — using a slightly different approach. In that study, the researchers first feed their electron micrographs into the computer algorithm, then ask human volunteers to check over the computer’s work and correct mistakes through a crowd-sourcing project known as EyeWire.
MIT’s Sebastian Seung has turned mapping the neurons of the retina into a social game, all in the name of neuroscience.
The retina is one of the most easily dissectible parts of the neurological system, and easy to isolate, but “looks like garbage,” Seung says, speaking at Wired 2012. “You need to look at under the microscope. It’s such a complicated structure that it’s safe to say that it’s more than just a camera; it’s a computer that performs some of the tasks of visual perception”. To figure that how it performs those tasks requires mapping the “tangles of spaghetti” that are the neuron pathways between the cells of the retina, a small part of the overall quest to understand the machine that is the brain.
Many people, Seung says, are uncomfortable with the idea of the brain being a machine that can be understood as just a collection of parts. “Most people I talk with hear you’re a neuroscientist [and] they ask lots of questions. But in the end the conversation comes around to you not being able to explain how the mind works without invoking the soul,” he says. The brain is so complicated, though, that it’s no surprise that people would think that there must be more to it than just key parts.
To know that, though, requires building “a parts list” like the kind you might get with some popular Swedish furniture, says Seung — “but the parts list of the retina has frustrated neuroscientists for decades”. It currently runs to a hundred types of cell and counting.
The type of cell that Seung is particularly interested in is the J cell, which plays a role in detecting motion — but neuroscientists aren’t sure how. That’s why Seung and his colleagues launched Eyewire, a site where any amateur neuroscientist can log on and scroll through 3D scans of retinal neurons. Users mark out the paths the neurons trace from cell to cell, correcting the guesses the computer might have got incorrect. There’s even an international leaderboard for people to compete with each other for points.
Seung says: “Professional scientists can’t do it alone — we need amateur neuroscientists. It’s important because there are questions that we all care about, like, why don’t our brains work properly? Sometimes there are neurological disorders like Parkinson’s where the brain decays and dies, but in other disorders we don’t know what’s going on. Some have speculated that it’s wired differently, but how can you know if it’s wired differently without mapping the wires?”
(Source: wired.co.uk)
EyeWire launches today with J Day!
It’s time to mobilize a global community of citizen neuroscientists to trace the 3D structure of J Cells and understand how retinal connectomes relate to visual perception.
A specific type of retinal neurons called J Cells respond to stimuli that move downward on the retina (which is the same as upward in the visual world). Neuroscientists do not currently understand how the neural circuits of the retina cause the J Cell to respond in this way. That’s one of the reasons we built EyeWire. By playing EyeWire, you map the 3D structure of retinal neurons and their connections, and collaborate with neuroscientists at MIT, the Max Planck Institute for Medical Research, and Harvard.
Over the past several months, members of Sebastian Seung’s lab at MIT have been hard at work making sure EyeWire allows users to accurately contribute to research. During our beta period, an average of 30 to 50 people played EyeWire each day. Collectively, EyeWirers have mapped over 160,000 individual cubes since the beta went live in spring. We hope to dwarf these numbers in the coming months.
Check out a short video from Sebastian Seung, who shares why we created EyeWire and how you can get involved.