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Fruit flies have special neurons that sense the wind to aid navigation

New Scientist

Specific neurons in fruit flies fire according to wind direction, helping them form a neural map of their surroundings. Algorithms inspired by this may be able to help robots to better navigate their environment. Tatsuo Okubo at Harvard Medical School and his colleagues wanted to determine how wind direction was characterised by a fruit fly's brain. While it is well known that wind direction affects the behaviour of insects, no one had yet developed a map of the neurons involved in this phenomenon for any animal.


How Your Body Knows What Time It Is - Issue 83: Intelligence

Nautilus

"The funny thing about life is that it's temporary; that is to say, temporary in the'temporal' sense of the word, meaning that all living things and all that we do are subject to the precepts and effects of time." Many organisms perform best at certain hours of the day. The slug species Arion subfuscus, living in almost total darkness, knowing nothing about the Gregorian calendar, lays its eggs between the last week of August and the first week of September.1 Bees forage for nectar, knowing the best times to visit the best fields and the exact timing of nectar secretions for individual species of flowers. In the mid-20th century, the Austrian Nobel laureate Karl von Frisch provided enormous insights on honeybee communication and foraging time. He discovered that bees have internal clocks that tell them not only where the nectar is to be found but also precisely when that food will be ready. "I know of no other living creature," he wrote in his book on bee language, "that learns so easily as the bee when, according to its'internal clock,' to come to the table."2 Even without a light clue, the plants were able to tell time.


Fruit fly inspires AI chip to help drones avoid obstacles, save power

#artificialintelligence

An NTHU team has developed an AI chip that follows the streamlined function of a fruit fly optic nerve. A major limitation for aerial drones is the tradeoff between weight and battery capacity, which limits their range and usefulness for applications such as agriculture and infrastructure inspection. To address this challenge, a multidisciplinary team at National Tsing Hua University in Hsinchu, Taiwan, has developed an artificial intelligence processor that mimics the optical nerves of a fruit fly. This AI chip enables unmanned aerial vehicles (UAVs) to automatically avoid obstacles while staying in an "ultra-power-saving mode," said the researchers. The team was led by professors Tang Kea-tiong of the Department of Electrical Engineering and Lo Chung-chuan of the Department of Life Sciences at National Tsing Hua University (NTHU).


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

#artificialintelligence

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.


Emergence of functional and structural properties of the head direction system by optimization of recurrent neural networks

arXiv.org Machine Learning

Recent work suggests goal-driven training of neural networks can be used to model neural activity in the brain. While response properties of neurons in artificial neural networks bear similarities to those in the brain, the network architectures are often constrained to be different. Here we ask if a neural network can recover both neural representations and, if the architecture is unconstrained and optimized, the anatomical properties of neural circuits. We demonstrate this in a system where the connectivity and the functional organization have been characterized, namely, the head direction circuits of the rodent and fruit fly. We trained recurrent neural networks (RNNs) to estimate head direction through integration of angular velocity. We found that the two distinct classes of neurons observed in the head direction system, the Ring neurons and the Shifter neurons, emerged naturally in artificial neural networks as a result of training. Furthermore, connectivity analysis and in-silico neurophysiology revealed structural and mechanistic similarities between artificial networks and the head direction system. Overall, our results show that optimization of RNNs in a goal-driven task can recapitulate the structure and function of biological circuits, suggesting that artificial neural networks can be used to study the brain at the level of both neural activity and anatomical organization.


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

#artificialintelligence

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

#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?


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?