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Georgia Tech researchers developed a neural network, RTNet, that mimics human decision-making processes, including confidence and variability, improving its reliability and accuracy in tasks like digit recognition. Working in the lab of Associate Professor Dobromir Rahnev in the School of Psychology, researchers are training neural networks to make decisions more like humans. This science of human decision-making is only just being applied to machine learning, but developing a neural network even closer to the actual human brain may make it more reliable.

Over the past few decades, earth scientists have grappled with the concept of solar geoengineering: cooling the rapidly warming planet by injecting particles high into the atmosphere to reflect sunlight, for example. Now, researchers are proposing a new way to battle the effects of climate change that could prove even more costly and controversial: glacial geoengineering, designed to slow sea level rise.

A white paper, released on 11 July by glaciologists, calls for boosting research into daring plans that would protect vulnerable ice sheets by building flexible barriers around them or drilling deep into them to slow their slippage into the sea.

These untested ideas are stirring up a backlash among glaciologists, some of whom view them not only as outlandishly expensive and logistically flawed but also as a distraction from the problem of reducing greenhouse gas emissions. In an article in Science, scientists, including School of Earth and Atmospheric Sciences Associate Professor Alex Robel, discuss the white paper and the distinction between supporting geoengineering and supporting its research. “I think the reality is that most people who will end up engaging in geoengineering research will do so because it increases the likelihood that geoengineering will actually happen,” says Robel.

On the timescale of sensory processing, neuronal networks have relatively fixed anatomical connectivity, while functional interactions between neurons can vary depending on the ongoing activity of the neurons within the network. In a paper published in Nature Communications, a team of researchers, including School of Mathematics Assistant Professor Hannah Choi, hypothesizes that different types of stimuli could lead those networks to display stimulus-dependent functional connectivity patterns. The team analyzed single-cell resolution electrophysiological data from the Allen Institute, with simultaneous recordings of stimulus-evoked activity from neurons across 6 regions of the mouse visual cortex. The work reveals unexpected stimulus-dependence regarding the way groups of neurons interact to process incoming sensory information.