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book of symbols
algorythms and oppressions

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See Yourself Sensing: Redefining Human Perception is an explosive and timely survey that explores the relationship between design, the body, technology and the senses over the last fifty years. Get ready to say goodbye to unconscious sensing and embrace cyborgs, post-humans, mediated reality and all manner of cutting edge sensory interventions like seeing with your tongue or plugging your nervous system directly into a computer. Astounding experiments with interaction design, cybernetics, neuroscience and art illustrate how we see and sense, and how artistic interpretation can undermine our fundamental perception of the world and ourselves.

The book presents the work of key practitioners in this field, from Rebecca Horn's mythical wearable structures and Stelarc's robotic body extensions, to Carsten Höllers' neurally interactive sculptures, as well as the work of artists who have emerged in the last five years, like Internet sensation Daito Manabe, Hyungkoo Lee and Michael Burton. The book explores projects such as solar-powered contact lenses that augment reality, LED eyelashes and goggles that allow one to communicate with electric fish, all created with the purpose of transforming and provoking the wearer's sensory experience. Madeline Schwartzman brings together this unique collection of images with provocative chapters and thoughtful descriptions of the concepts informing the work in this book.

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See Yourself Sensing: Redefining Human Perception is an explosive and timely survey that explores the relationship between design, the body, technology and the senses over the last fifty years. Get ready to say goodbye to unconscious sensing and embrace cyborgs, post-humans, mediated reality and all manner of cutting edge sensory interventions like seeing with your tongue or plugging your nervous system directly into a computer.

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Log In ‹ SCP-0146 / Digital Fabrication Studio - WordPress

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brain - music

3 books

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Global warming is perhaps the most dramatic example of what Timothy Morton calls "hyperobjects" - entities of such vast temporal and spatial dimensions that they defeat traditional ideas about what a thing is in the first place. In this book, Morton explains what hyperobjects are and their impact on how we think, how we coexist with one another and with nonhumans, and how we experience our politics, ethics, and art.

Moving fluidly between philosophy, science, literature, visual and conceptual art, and popular culture, the book argues that hyperobjects show that the end of the world has already occurred in the sense that concepts such as world, nature, and even environment are no longer a meaningful horizon against which human events take place. Instead of inhabiting a world, we find ourselves inside a number of hyperobjects, such as climate, nuclear weapons, evolution, or relativity. Such objects put unbearable strains on our normal ways of reasoning.

Insisting that we have to reinvent how we think to even begin to comprehend the world we now live in, Hyperobjects takes the first steps, outlining a genuinely postmodern ecological approach to thought and action.

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Check out this great listen on Audible.com. Global warming is perhaps the most dramatic example of what Timothy Morton calls "hyperobjects" - entities of such vast temporal and spatial dimensions that they defeat traditional ideas about what a thing is in the first place. In this book, M...

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"A provocative and entertaining magical mineral tour through the life and afterlife of bone." ( Wall Street Journal)
Our bones have many stories to tell, if you know how to listen.
Bone is a marvel, an adaptable and resilient building material developed over 500 million years of evolutionary history. It gives our bodies their shapes and the ability to move. It grows and changes with us, an undeniable document of who we are and how we lived. Arguably, no other part of the human anatomy has such rich scientific and cultural significance, both brimming with life and a potent symbol of death.

Brian Switek is a charming and enthusiastic osteological raconteur. In this natural and cultural history of bone, he explains where our skeletons came from, what they do inside us, and what others can learn about us when these wondrous assemblies of mineral and protein are all we've left behind.

Bone is as embedded in our culture as it is in our bodies. Our species has made instruments and jewelry from bone, treated the dead like collectors' items, put our faith in skull bumps as guides to human behavior, and arranged skeletons into macabre tributes to the afterlife. Switek makes a compelling case for getting better acquainted with our skeletons, in all their surprising roles. Bridging the worlds of paleontology, anthropology, medicine, and forensics, Skeleton Keys illuminates the complex life of bones inside our bodies and out.

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Check out this great listen on Audible.com. "A provocative and entertaining magical mineral tour through the life and afterlife of bone." (Wall Street Journal) Our bones have many stories to tell, if you know how to listen. Bone is a marvel, an adaptable and resilient building material d...

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In this New York Times bestseller, an award-winning journalist uses ten maps of crucial regions to explain the geo-political strategies of the world powers-"fans of geography, history, and politics (and maps) will be enthralled" ( Fort Worth Star-Telegram).

Maps have a mysterious hold over us. Whether ancient, crumbling parchments or generated by Google, maps tell us things we want to know, not only about our current location or where we are going but about the world in general. And yet, when it comes to geo-politics, much of what we are told is generated by analysts and other experts who have neglected to refer to a map of the place in question.

All leaders of nations are constrained by geography. In "one of the best books about geopolitics" ( The Evening Standard), now updated to include 2016 geopolitical developments, journalist Tim Marshall examines Russia, China, the US, Latin America, the Middle East, Africa, Europe, Japan, Korea, and Greenland and the Arctic-their weather, seas, mountains, rivers, deserts, and borders-to provide a context often missing from our political reportage: how the physical characteristics of these countries affect their strengths and vulnerabilities and the decisions made by their leaders.

Offering "a fresh way of looking at maps" ( The New York Times Book Review), Marshall explains the complex geo-political strategies that shape the globe. Why is Putin so obsessed with Crimea? Why was the US destined to become a global superpower? Why does China's power base continue to expand? Why is Tibet destined to lose its autonomy? Why will Europe never be united? The answers are geographical. "In an ever more complex, chaotic, and interlinked world, Prisoners of Geography is a concise and useful primer on geopolitics" ( Newsweek) and a critical guide to one of the major determining factors in world affairs.

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prisoners of geography: ten maps that explain everything about the world (politics of place)

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political science / international relations / diplomacy

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geography; geopolitical; human geography; world history; current events; international relations; bestseller; world politics; international politics; gifts for teacher; historical geography; history; geopolitics; best map books; society; social history; non-fiction; middle east; russia and china; environmental policy; history books for adults; european history; climate change; global warming; current affairs; global affairs

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political science / international relations / diplomacy

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Prisoners of Geography: Ten Maps That Explain Everything About the World (Politics of Place)

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Buy Prisoners of Geography: Ten Maps That Explain Everything About the World (Politics of Place) on Amazon.com ✓ FREE SHIPPING on qualified orders

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Synesthesia

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Here's your chance to see how people with synesthesia perceive letters and numbers

Images: Bernadette Sheridan

F or as long as I can remember, I've seen letters and numbers as colors. It's a form of synesthesia called grapheme-color synesthesia, and for me, this translating from symbols to colors happens most often with names.

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What Color Is Your Name? A New Synesthesia Tool Will Show You.

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F or as long as I can remember, I've seen letters and numbers as colors. It's a form of synesthesia called grapheme-color synesthesia, and for me, this translating from symbols to colors happens most often with names.

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https://www.medicalnewstoday.com/articles/320019

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This section gives a brief introduction to how EEG electrodes are set up, what they mean and what the doctors can learn from reading an EEG.

How are the electrodes arranged?

An EEG technicial places the electrodes in specific areas, according to internationally agreed-upon criteria.

To find where to put the electrodes, first the technician marks four points on your head - the nasion (indentation between the forehead and the nose), the inion (ridge that can be felt in the middle of the back of the skull, over the occipital area), and the preauricular points on both sides of the head (indentations above the outer part of the ear openings).

The electrode are then placed in many areas on the head, at specific locations and distances from these landmarks or points listed above.

Sometimes other electrodes (sphenoidal and suboccipital, for instance) are placed to increase the chance of recording EEG waves from areas that may be too small or too deep to be recorded by the usual electrodes.

Often an electrode is placed on the chest to record the EKG (electrocardiogram) which is a a record of the heartbeat.

What do the letters and numbers mean?

The names of the electrode sites use alphabetical abbreviations that identify the lobe or area of the brain which each electrode records from:

The localization of the brain waves within the brain regions or lobes is further narrowed by adding electrodes, which are given numbers such as T3, T4, P3, P4. Even numbers identify electrode positions on the right side of the head, and odd numbers refer to the left side. The label "z" points to electrode sites in the midline of the head. For example, Cz refers to the midline central region of the head.

How are the electrodes related to each other?

The EEG records brain waves using equipment called amplifiers and by looking at the information from the electrodes in different combinations. These combinations of electrodes are called 'montages'.

In bipolar montages, consecutive pairs of electrodes are linked by connecting the electrode input 2 of one channel to input 1 of the subsequent channel, so that adjacent channels have one electrode in common. The bipolar chains of electrodes may be connected going from front to back (longitudinal) or from left to right (transverse).

Another type of montage is the referential montage. In this type, various electrodes are connected to input 1 of each amplifier and a reference electrode is connected to input 2 of each amplifier. Ideally, inactive electrodes (ones that are uninvolved in the electrical field being studied) are chosen as references.

What does the doctor learn from looking at the EEG?

Being able to locate the origin of electrical activity ("localization") is critical to being able to interpret the EEG tracings meaningfully.

Localizing normal or abnormal brain waves in bipolar montages is usually accomplished by identifying "phase reversal," a deflection of the two channels within a chain pointing to opposite directions.

In a referential montage, all channels may show deflections or movements in the same direction. If the electrical activity at the active electrodes is positive when compared to the activity at the reference electrode, the deflection will be downward. Electrodes where the electrical activity is the same as at the reference electrode will not show any deflection. In general, the electrode with the largest upward deflection represents the maximum negative activity in a referential montage.

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This section gives a brief introduction to how EEG electrodes are set up, what they mean and what the doctors can learn from reading an EEG. How are the electrodes arranged? An EEG technicial places the electrodes in specific areas, according to internationally agreed-upon criteria.

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Brain noise

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Introduction

At a sleep research symposium in January 2020, Janna Lendner presented findings that hint at a way to look at people's brain activity for signs of the boundary between wakefulness and unconsciousness. For patients who are comatose or under anesthesia, it can be all-important that physicians make that distinction correctly. Doing so is trickier than it might sound, however, because when someone is in the dreaming state of rapid-eye movement (REM) sleep, their brain produces the same familiar, smoothly oscillating brain waves as when they are awake.

Lendner argued, though, that the answer isn't in the regular brain waves, but rather in an aspect of neural activity that scientists might normally ignore: the erratic background noise.

Some researchers seemed incredulous. "They said, 'So, you're telling me that there's, like, information in the noise?'" said Lendner, an anesthesiology resident at the University Medical Center in Tübingen, Germany, who recently completed a postdoc at the University of California, Berkeley. "I said, 'Yes. Someone's noise is another one's signal.'"

Lendner is one of a growing number of neuroscientists energized by the idea that noise in the brain's electrical activity could hold new clues to its inner workings. What was once seen as the neurological equivalent of annoying television static may have profound implications for how scientists study the brain.

Skeptics used to tell the neuroscientist Bradley Voytek that there was nothing worth studying in these noisy features of brain activity. But his own studies of changes in electrical noise as people age, as well as previous literature on statistical trends in irregular brain activity, convinced him that they were missing something. So he spent years working on a way to help scientists rethink their data.

"It's insufficient to go up in front of a group of scientists and say, 'Hey, I think we've been doing things wrong,'" said Voytek, an associate professor of cognitive science and data science at the University of California, San Diego. "You've got to give them a new tool to do things" differently or better.

In collaboration with neuroscientists at UC San Diego and Berkeley, Voytek developed software that isolates regular oscillations - like alpha waves, which are studied heavily in both sleeping and waking subjects - hiding in the aperiodic parts of brain activity. This gives neuroscientists a new tool to dissect both the regular waves and the aperiodic activity in order to disentangle their roles in behavior, cognition and disease.

The phenomenon that Voytek and other scientists are investigating in a variety of ways goes by many names. Some call it "the 1/ f slope" or "scale-free activity"; Voytek has pushed to rebrand it "the aperiodic signal" or "aperiodic activity."

It's not just a quirk of the brain. The patterns that Lendner, Voytek and others look for are related to a phenomenon that scientists started noticing in complex systems throughout the natural world and technology in 1925. The statistical structure crops up mysteriously in so many different contexts that some scientists even think it represents an undiscovered law of nature.

Although published studies have looked at arrhythmic brain activity for more than 20 years, no one has been able to establish what it really means. Now, however, scientists have better tools for isolating aperiodic signals in new experiments and looking more deeply older data, too. Thanks to Voytek's algorithm and other methods, a flurry of studies published in the last few years have run with the idea that aperiodic activity contain hidden treasures that may advance the study of aging, sleep, childhood development and more.

What Is Aperiodic Activity?

Our bodies groove to the familiar rhythms of heartbeats and breaths - persistent cycles essential to survival. But there are equally vital drumbeats in the brain that don't seem to have a pattern, and they may contain new clues to the underpinnings of behavior and cognition.

When a neuron sends a chemical called glutamate to another neuron, it makes the recipient more likely to fire; this scenario is called excitation. Conversely, if a neuron spits out the neurotransmitter gamma-aminobutyric acid, or GABA, the recipient neuron becomes less likely to fire; that's inhibition. Too much of either has consequences: Excitation gone haywire leads to seizures, while inhibition characterizes sleep and, in more extreme cases, coma.

To study the delicate balance between excitation and inhibition, scientists measure the brain's electrical activity with electroencephalography, or EEG. Cycles in excitation and inhibition form waves that have been linked to different mental states. Brain emissions at around 8 to 12 hertz, for example, form the alpha wave pattern associated with sleep.

But the brain's electrical output doesn't produce perfectly smooth curves. Instead, the lines jitter as they slope up toward peaks and down toward troughs. Sometimes brain activity has no regularity and instead looks more like electrical noise. The "white noise" component of this is truly random like static, but some of it has a more interesting statistical structure.

It's those imperfections in the smoothness, and in the noise, that interest neuroscientists like Voytek. "It's random, but there's different kinds of random," he said.

To quantify this aperiodic activity, scientists break down the raw EEG data, much as a prism can decompose a sunbeam into a rainbow of different colors. They first employ a technique called Fourier analysis. Any set of data plotted over time can be expressed as a sum of trigonometric functions like sine waves, which can be expressed in terms of their frequency and amplitude. Scientists can plot the wave amplitudes at different frequencies in a graph called a power spectrum.

The amplitudes for power spectra are usually plotted in logarithmic coordinates because of the wide range in their values. For purely random white noise, the power spectrum curve is relatively flat and horizontal, with a slope of zero, because it's about the same at all frequencies. But neural data produces curves with a negative slope such that lower frequencies have higher amplitudes, and the intensity drops off exponentially for higher frequencies. This shape is called 1/ f, referring to that inverse relationship between the frequency and the amplitude. Neuroscientists are interested in what the flatness or steepness of the slope might indicate about the brain's inner workings.

Analyzing EEG data in this way is analogous to looking at the sound waves from an audio recording made on a bridge over a highway, explains Lawrence Ward, a cognitive neuroscientist at the University of British Columbia. The hum of the tires from random passing cars would produce aperiodic background features, but nearby trains that sound a whistle every 10 minutes would generate a periodic signal with peaks in the data louder than the background. A sudden one-time event like a long horn honk or a vehicle collision would produce a noticeable spike in the sound wave, contributing to the overall 1/ f slope.

Awareness of the 1/ f phenomenon dates back to a 1925 paper by J.B. Johnson of Bell Telephone Laboratories, who was looking at noise in vacuum tubes. The German scientist Hans Berger published the first human EEG study just four years later. Neuroscience research in subsequent decades focused heavily on the prominent periodic waves in brain activity. Yet 1/ f fluctuations were found in all kinds of electrical noise, stock market activity, biological rhythms, and even pieces of music - and no one knew why.

Samuel Velasco/Quanta Magazine; source: Thomas Donoghue

Introduction

Perhaps because it seemed so universal, many biologists dismissed the idea that looking at noise through the lens of 1/ f characteristics could yield useful signals; they thought it might be a form of noise from the scientific instruments used, wrote Biyu J. He, an assistant professor of neurology, neuroscience and physiology at New York University Grossman School of Medicine, in a 2014 review in Trends in Cognitive Sciences.

But He and others debunked that idea through experiments controlling for instrument noise, which turned out to be much smaller in magnitude than aperiodic brain activity. In a 2010 paper in Neuron, He and her colleagues also found that while EEG readouts, seismic waves in the ground, and stock market fluctuations all exhibit 1/ f trends, the data from these sources exhibits different higher-order statistical structures. That insight put a dent in the idea that a single law of nature generates aperiodic signals in everything.

However, it isn't a completely settled question. Ward has found mathematical commonalities in different contexts and believes something fundamental could be going on behind the scenes.

Either way, both Ward and He agree it's worth probing deeper in the brain.

"For decades, brain activity contained in the '1/ f ' slope has been deemed unimportant and was often removed from analyses in order to emphasize brain oscillations," He wrote in the 2014 paper. "However, in recent years, increasing evidence suggests that scale-free brain activity contributes actively to brain functioning."

New Signals From Noise

Voytek fell into the topic of aperiodic signals somewhat accidentally: He originally wanted to model and remove white noise from EEG data. But as he hacked away at a code to pull out noise, he started paying more attention to what was interesting within it.

The brains of older adults seem to have more aperiodic activity than those of younger adults, Voytek found in a 2015 study with his doctoral adviser Robert Knight, a professor of neuroscience at Berkeley. Voytek and Knight observed that as the brain ages, it is dominated more by white noise. They also found correlations between this noise and age-related working memory decline.

Voytek wanted neuroscientists to have software that could more easily and automatically isolate the periodic and aperiodic features in any data set, including old ones, and help researchers look for meaningful 1/ f trends. So he and his team wrote a program for an algorithm that could do that.

The demand for a tool like this became clear immediately. After Voytek and colleagues posted their code to the website biorxiv.org on April 11, 2018, it received nearly 2,000 downloads within the month - a big hit for a niche neuroscience computational tool. In November of that year, Voytek moderated a standing-room-only talk at the Society for Neuroscience conference on how to use it. Because of its popularity, he organized a last-minute follow-up session, where his lab team provided tech support to dozens of interested scientists. The tutorial and e-mail exchanges led to new collaborations.

One of those collaborations was Lendner's study of markers for arousal during sleep, published in the online journal eLife in July 2020. With Voytek's software, Lendner and her colleagues found that in the aperiodic noise of test subjects' EEGs, the high-frequency activity dropped off faster during REM sleep than when they were awake. In other words, the slope of the power spectrum was steeper.

Courtesy of Janna Lendner

Introduction

In their paper, Lendner and her co-authors argue that aperiodic signals can serve as a unique signature to measure a person's state of consciousness. A new objective marker like this could help to improve the practice of anesthesia and treatments for coma patients.

Other published studies that used Voytek's code included investigations of ADHD medication efficacy and studies of sex-based differences in brain activity in people with autism. The code was published in a peer-reviewed journal for the first time - Nature Neuroscience - in November 2020; Thomas Donoghue of UC San Diego, Matar Haller (then at Berkeley) and Erik Peterson (then at UC San Diego) were co-first authors of the paper, with Avgusta Shestyuk of Berkeley serving as co-senior author with Voytek. They and other members of the team demonstrated the code's performance on simulated data and its potential to reveal new findings.

Natalie Schaworonkow, a postdoctoral fellow in Voytek's lab, usually researches regular oscillations like alpha waves, "which are more beautiful than the aperiodic signal," she said, making Voytek laugh in our shared Zoom call. But when her interests recently turned to the infant brain and the electrical patterns that are signatures of its cognitive development, she was faced with a problem, because infants do not produce these elegant alpha waves; exactly when and how the waves start to appear is an open question.

She used the algorithm to analyze an open EEG data set of infant brain activity. In a new paper published in Developmental Cognitive Neuroscience, Schaworonkow and Voytek found large changes in aperiodic activity during the first seven months of life. More research is needed, however, to figure out whether this activity reflects greater engagement in tasks as children grow up or just increases in gray matter density.

Voytek's code has driven a lot of recent research, but it isn't the only game in town for aperiodic noise analysis. In 2015, when Haiguang Wen of the tech company Nvidia and Zhongming Liu of the University of Michigan both worked at Purdue University (Wen was a research assistant and Liu was an associate professor), they published a different approach to isolating the periodic from the aperiodic components in brain activity, called irregular-resampling auto-spectral analysis (IRASA). Meanwhile, Biyu He has been working on the topic since before either of these tools arrived on the scene; so too did the late neuroscientist Walter J. Freeman, whose work inspired Voytek. It's possible to do this kind of work by hand, though it is far more time-consuming.

Having a tool that allows neuroscientists to easily examine their data in terms of periodic and aperiodic signals is important because the data itself is just a set of numbers gathered over a specific period of time. A graph of points by itself doesn't say anything about brain functioning or malfunctioning.

"Interpretation is what matters in neuroscience, right? Because that's what we make clinical decision-making off of and drug development and all of this kind of stuff," Voytek said. A huge wealth of data sets in the literature has the potential to yield new insights when reexamined in this way, he said, and "we haven't been interpreting them as richly as we should."

What Does It Mean?

A big limitation in scientists' exploration of these aperiodic features is that no one knows exactly what causes them physiologically. More research is needed to clarify the respective contributions of different neurotransmitters, neural circuits and large-scale network interactions, said Sylvain Baillet, a professor of neurology and neurosurgery, biomedical engineering, and computer science at McGill University.

"The causes and the sources are still not identified," Baillet said. "But we have to do this research to accumulate knowledge and observations."

One theory is that aperiodic signals somehow reflect the delicate balance between excitation and inhibition that the brain needs to keep itself healthy and active. Too much excitation may overload the brain, while too much inhibition may put it to sleep, Lendner said.

Knight thinks that explanation is on the right track. "I wouldn't want to say I'm positive it's an inhibition-excitation ratio change, but I think it's the most parsimonious explanation," he said.

An alternative idea is that the aperiodic signals simply reflect the brain's physical organization.

Based on how other physical systems reflect 1/ f behaviors, Ward thinks there could be some kind of structural, hierarchical relationship in the brain that gives rise to the aperiodic activity. For example, this might arise from the way that huge numbers of neurons organize themselves into groups, which then form larger regions that work together.

Brain activity related to 1/ f trends may be ideally suited to processing sensory input in the natural environment, since that often exhibits 1/ f-type fluctuations, He said. Her 2018 study in The Journal of Neuroscience explores how the brain is able to make predictions about sounds that also have 1/ f properties, suggesting that aperiodic activity "is involved in processing and predicting naturalistic stimuli," she said in an email. It isn't surprising to her that music, from jazz to Bach, can also have 1/ f properties - after all, music is a creation of the human brain.

To test hypotheses about where aperiodic signals come from, Voytek said, researchers need to look more closely at what kinds of neural circuitry could give rise to them. Neuroscientists can then try to link sites with those circuits to the brain's overall physiology for a better idea of which neural mechanisms generate specific activity patterns, and to predict how the aperiodic and periodic signals would look in different brain disorders.

Voytek is also hoping to do more large-scale studies that apply the code to existing data sets to tease out untapped signals.

Lendner and Knight are currently analyzing data on coma patients at the University of Alabama to see if aperiodic activity correlates with how a coma evolves. Their prediction is that if a person is coming out of a coma, a rise in high-frequency activity in the brain will show up as a change in the 1/ f slope. The preliminary results are promising, Lendner said.

For Baillet, the aperiodic signals in the brain are a bit like dark matter, the invisible scaffolding of the universe that interacts with normal matter only through gravity. We don't understand what it's made of or what its properties are, but it's out there in the celestial background, furtively holding the Milky Way together.

Scientists haven't figured out what causes these aperiodic signals yet, but they too may reflect an essential support structure for the universe in our heads. Something mysterious may help tip our minds from waking life into slumber.
This article was reprinted on Wired.com.

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What Dust From Space Tells Us About Ourselves

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Brain's 'Background Noise' May Hold Clues to Persistent Mysteries

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By digging out signals hidden within the brain's electrical chatter, scientists are getting new insights into sleep, aging and more.

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Renderings by D. Berger (MIT) based on data collected by N. Kasthuri, K. Hayworth, J. C. Tapia, R. Schalek, and J. Lichtman (Harvard University). - See more at WiredDifferently.

EyeWire is a game to map the brain from Dr. Sebastian Seung's Computational Neuroscience Lab at Princeton University. EyeWire mobilizes gamers from the around the world to create 3D reconstructions of neurons by interacting with a deep convolutional network. Seung and his team developed an artificial intelligence algorithm that allows players to reconstruct neurons from the retina in volumetric segments by scrolling through a cube (measuring about 4.5 microns per side or ~10x smaller than the average width of a human hair) and map branches of a neuron from one side of a cube to the other, like a 3D puzzle.

EyeWire is played by over 150,000 people from 145 countries. Sign up to try it for yourself!

Read more about EyeWire on the EyeWire Blog, and check out the research paper published in Nature, where authorship was given to the EyeWire player community. Learn about the Connectome in this TED talk by Dr. Sebastian Seung, founder of EyeWire.
EyeWire is jointly operated by a nonprofit named WiredDifferently, and by the Seung Lab at Princeton University.

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Renderings by D. Berger (MIT) based on data collected by N. Kasthuri, K. Hayworth, J. C. Tapia, R. Schalek, and J. Lichtman (Harvard University). - See more at WiredDifferently. EyeWire is a game to map the brain from Dr. Sebastian Seung's Computational Neuroscience Lab at Princeton University.