The phrase, "reading is fundamental," is a slogan, a meme, and the name of a children's literacy nonprofit. For those of us who can read, especially with a fluidity that feels almost like an extension of our own thinking, the expression's rationale is simple: the foundation of our composed world is the written word. After all, the ability to read is necessary to send a text, apply for a job, or even to identify our favorite products after they've undergone yet another rebranding. Illiteracy in the modern world is like trying to navigate the high seas with neither a knowledge of the stars, a map or a compass--possible, but needlessly difficult. But new research suggests that learning to read does more than make life easier: it literally changes how the brain works by increasing connectivity between its regions.
Computer brains are becoming more intelligent -- we've been trying to work out who is smartest and sharpest since since the dawn of video games if not before. Computer'brains' in the world of Artificial Intelligence don't actually function organically, like a human brain, obviously. But as we continue to build new and ever more powerful layers of functionality into the machine brain, they can start to'ape' some of our human imperfections and nuances in an attempt to be more like us. Software application developers (and their IT'Ops' operations buddies) are working hard to move statistical models into computer brains and advance not just AI, but the inextricably closely related area of machine learning which helps feed the practice of'automation', which in and of itself has become the darling buzzword of the IT industry in recent times. Data intelligence firms like Elastic are building machine learning functions into their software as fast as they can.
The military wants artificial intelligence, but it's not intending to cook it up from scratch. Instead, in a recent solicitation, DARPA asked for proposals to build A.I. based on insect brains. The program seeks to build A.I. that is smaller and more efficient than normal software. Unlike us, insects operate almost entirely based on simple stimuli. Moths, for instance, are so programmed to navigate based on the direction of light that they occasionally navigate directly into lightbulbs.
A team of researchers from the Brain Research Institute of the University of Zurich and the Swiss Federal Institute of Technology (ETH) have developed a fully automated brain registration method that could be used to segment brain regions of interest in mice. Neuroscientists are always seeking out new methods of exploring the structure and function of different brain regions, which are initially applied on animals but could eventually lead to important discoveries about the organization of the human brain. "My lab aims to reveal how the mammalian brain develops its abilities to process and react to sensory stimuli," Theofanis Karayannis, one of the researchers who carried out the study told Tech Xplore. "Most of the work we do is on the experimental side, utilizing the mouse as a model system and techniques that range from molecular-genetic to functional and anatomical." This study is part of a larger project, which also includes "Exploring Brain-wide Development of Inhibition through Deep Learning," a study in which Karayannis and his colleagues use deep learning algorithms to comprehensively track the so-called inhibitory neurons over time in order to gauge the development of capabilities of the brain at specific points in time.
Thanks to deep learning, the tricky business of making brain atlases just got a lot easier. Brain maps are all the rage these days. From rainbow-colored dots that highlight neurons or gene expression across the brain, to neon "brush strokes" that represent neural connections, every few months seem to welcome a new brain map. Without doubt, these maps are invaluable for connecting the macro (the brain's architecture) to the micro (genetic profiles, protein expression, neural networks) across space and time. Scientists can now compare brain images from their own experiments to a standard resource.