at the Brain Mind Institute of the Ecole Polytechnique Fédérale de Lausanne (EPFL) in Switzerland when her work was published in Science (subscription required) in August of this year. Lenggenhager was in the second year of her Ph.D. It's very exciting to do an early step" in an emerging area of science. "For me, it is especially interesting for that reason. It's not a common subject for scientific research, she admits, but she sees that as an advantage. Lenggenhager works on bodily self-awareness and multisensory integration by the brain, using out-of-body experiences-the disturbing feeling of having one's self and body detached from each other-as a model for investigation. Intrigued by the experiences of some neurological patients, Lenggenhager decided "to study how flexible and how prone to illusion even the very self is," she says in an interview with Science Careers. But 27-year-old Swiss neuropsychologist Bigna Lenggenhager chose to step into the world of illusions for her doctoral research.
The potential of this form to explain difficult concepts is unmatched and underused.įor another comic-style explanation (this time, an art analysis), see Tom Oreb's Portrait of Ward Kimball.Whether their work is in human genes, dwarf planets, or computer chips, many scientists have this in common: What they study is tangibly out there, somewhere. The comic form is about sequences of tightly-integrated words and pictures, together conveying a message more powerfully than the sum of their parts. Let alone superheroes or anthropomorphic animals. Whereas, for the material near the end, pictures are coupled to entire paragraphs, and the pictures serve as examples for concepts conveyed by words:įinally, "comic-style" needn't mean characters, dialog, word balloons, or sound effects. For the description of the algorithm, pictures are coupled to individual sentences or even fragments, and the words serve to explain the action in the pictures: The roles of pictures and prose can be fluid. For more on subtle supplemental interactivity, see Explorable Explanations. This interaction is optional and supplemental - it's available for a reader who wants the clarification, but can be skipped by a reader who already understands (or doesn't care). I've used a small amount of lightweight interactivity (via hover-sliders ) to allow the reader to see the progression of the algorithm, in-place, over many steps. Illustrating the state of an algorithm at each step can make the description dramatically easier to follow. In order to understand what the algorithm is doing, we have to "play computer" and imagine the state in our head. When an algorithm is described in prose (or code), we are typically given only the rules of the system - we can't see the data or the state. The word count (and reading time) of a graphic novel is far less than that of a comparable prose novel. Paradoxically, making lavish use of space can result in briefer writing - illustrations allow for terser prose. We can use as many pixels as we need to explain a concept. I apologize for omitting the good stuff.)Ī computer screen is not space-constrained like a physical journal. (The paper goes on to identify several real-life networks as small-world, and discuss the implications for dynamical systems. In particular, infectious diseases spread more easily in small-world networks than in regular lattices. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation). We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. But many biological, technological and social networks lie somewhere between these two extremes.
Ordinarily, the connection topology is assumed to be either completely regular or completely random. Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems.
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