fruit fly


Google's giant map of brain connectivity is a beautiful tangled mess

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Researchers from Google and Janelia Research Campus made a complex map of the brain of a fruit fly. Tiny brain, big science: Researchers from Google and the Howard Hughes Medical Institute's Janelia Research Campus have created an in-depth 3D map of a fruit fly's brain, which they say could teach us more about how the organ controls learning, memory and other behaviors. It's the largest brain map of this type -- called a connectome -- ever created, for any animal, according to the research project website and a paper describing the work. The map covers a large portion of the fly's brain, including the circuits used for learning, navigation, visual processing and possibly sleep, says the paper. It includes about 25,000 neurons, along with 20 million chemical synapses found between them, and it required complex imaging technology and deep learning algorithms, according to an article from the research campus.


The Brain Cells That Guide Animals - Issue 81: Maps

Nautilus

It may seem absurd to compare a tiny fruit fly's brain to that of a majestic elephant. Yet it is the dream of many neuroscientists to find deep rules that very different brains share. As Gilles Laurent, a neuroscientist at the Max Planck Institute for Brain Research in Frankfurt, Germany, who has studied a variety of animals, from locusts to turtles, has said, "Neural responses can be described by the same mathematical operation … in completely different systems." Vivek Jayaraman, a researcher at the Howard Hughes Medical Institute's Janelia Research Campus, and a former student of Laurent's, believes that neuroscientists are on the verge of identifying some of these deep neural rules. Grasping them would advance another neuroscientific dream: to be able to predict animal behavior as easily as Newton could predict the behavior of a moving object.


The Brains Cells That Guide Animals - Facts So Romantic

Nautilus

It may seem absurd to compare a tiny fruit fly's brain to that of a majestic elephant. Yet it is the dream of many neuroscientists to find deep rules that very different brains share. As Gilles Laurent, a neuroscientist at the Max Planck Institute for Brain Research in Frankfurt, Germany, who has studied a variety of animals, from locusts to turtles, has said, "Neural responses can be described by the same mathematical operation…in completely different systems." Vivek Jayaraman, a researcher at the Howard Hughes Medical Institute's Janelia Research Campus, and a student of Laurent's, believes that neuroscientists are on the verge of identifying some of these deep neural rules. Grasping them would advance another neuroscientific dream: to be able to predict animal behavior as easily as Newton could predict the behavior of a moving object.


Argonne Team Looks to Insect Brains as Models for Computer Chip Innovation

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Scientists at the Energy Department's Argonne National Laboratory have pioneered a cutting-edge neuromorphic computer chip--modeled off the brains of bees, fruit flies and other insects--that can rapidly learn, adapt and use substantially less power than its conventional computer chip counterparts. The physicist leading an interdisciplinary team that developed the state-of-the-art design recently spoke to Nextgov about the chips' potential to advance artificial intelligence. "If we start from a biology standpoint, we use ourselves, humans, as a model for intelligent systems, of course. But there are many other branches that evolution has taken where you can sort of reach big computational power," Angel Yanguas-Gil, principal materials scientist in Argonne's Applied Materials division, said. "Insects are one of these areas."


How ants, bees, and fruit flies can be the next big buzz in artificial intelligence

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And on Nov. 2, 2018, NASA's Voyager 2 spacecraft crossed into the vastness of interstellar space, following Voyager 1, which made the leap six years earlier. Since their launch in 1977, the two probes have traveled more than 11 billion miles across the solar system, lasting much longer than scientists anticipated. Powered by plutonium and drawing 400 watts of power each to run their electronics and heat, the probes still snap photos and send them back to NASA. After 42 years, though, only six of Voyager 2's 10 instruments still work, and NASA scientists expect the probe will go dark in 2025, well before it leaves our Solar system. But what if Voyager 2 needed only a couple of watts of power?


How ants, bees, and fruit flies can be the next big buzz in artificial intelligence

#artificialintelligence

And on Nov. 2, 2018, NASA's Voyager 2 spacecraft crossed into the vastness of interstellar space, following Voyager 1, which made the leap six years earlier. Since their launch in 1977, the two probes have traveled more than 11 billion miles across the solar system, lasting much longer than scientists anticipated. Powered by plutonium and drawing 400 watts of power each to run their electronics and heat, the probes still snap photos and send them back to NASA. After 42 years, though, only six of Voyager 2's 10 instruments still work, and NASA scientists expect the probe will go dark in 2025, well before it leaves our Solar system. But what if Voyager 2 needed only a couple of watts of power?


Deep Learning Algorithms Reconstruct The Brain Of A Fruit Fly

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Researchers from the interdisciplinary fields of computational sciences and neuro sciences usually take the anthropomorphic design approach to mimic human understanding of conceptual foundations. The researchers usually suggest this bottom-up approach to understand intelligent architectures because simple nervous systems(number of neurons that can be mapped) found in nature, like that of nematodes, are biophysically simulated to check how well they incorporate biomechanics in a simulated environment. Last year, a study aimed at AI safety by Gopal Sarma and his team in collaboration with Vicarious AI built realistic simulations of simple organisms like fruit flies and zebrafish. The roots of this approach are structured in neuropsychology. Recently, the field of connectomics added another tool to its diverse portfolio gathered from rich interdisciplinary advantage.


Forget Finding Nemo: This AI can identify a single zebrafish out of a 100-strong shoal

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AI systems excel in pattern recognition, so much so that they can stalk individual zebrafish and fruit flies even when the animals are in groups of up to a hundred. To demonstrate this, a group of researchers from the Champalimaud Foundation, a private biomedical research lab in Portugal, trained two convolutional neural networks to identify and track individual animals within a group. The aim is not so much to match or exceed humans' ability to spot and follow stuff, but rather to automate the process of studying the behavior of animals in their communities. "The ultimate goal of our team is understanding group behavior," said Gonzalo de Polavieja. "We want to understand how animals in a group decide together and learn together."


Artificial fly brain can tell who's who

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In an interdisciplinary project funded by a Canadian Institute for Advanced Research (CIFAR) Catalyst grant, researchers at the University of Guelph and the University of Toronto, Mississauga combined expertise in fruit fly biology with machine learning to build a biologically-based algorithm that churns through low-resolution videos of fruit flies in order to test whether it is physically possible for a system with such constraints to accomplish such a difficult task. Fruit flies have small compound eyes that take in a limited amount of visual information, an estimated 29 units squared. The traditional view has been that once the image is processed by a fruit fly, it is only able to distinguish very broad features. But a recent discovery that fruit flies can boost their effective resolution with subtle biological tricks has led researchers to believe that vision could contribute significantly to the social lives of flies. This, combined with the discovery that the structure of their visual system looks a lot like a Deep Convolutional Network (DCN), led the team to ask: "can we model a fly brain that can identify individuals?"


Chess, a Drosophila of reasoning

Science

The recent world chess championship saw Magnus Carlsen defend his title against Fabiano Caruana. But it was not a contest between the two strongest chess players on the planet, only the strongest humans. Soon after I lost my rematch against IBM's Deep Blue in 1997, the short window of human-machine chess competition slammed shut forever. Unlike humans, machines keep getting faster, and today a smartphone chess app can be stronger than Deep Blue. But as we see with the AlphaZero system (see pages 1118 and 1140), machine dominance has not ended the historical role of chess as a laboratory of cognition.