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Research by neuroscientists at the University of Chicago shows how short-term, working memory uses networks of neurons differently depending on the complexity of the task at hand.
The researchers used modern artificial intelligence (AI) techniques to train computational neural networks to solve a range of complex behavioral tasks that required storing information in short term memory. The AI networks were based on the biological structure of the brain and revealed two distinct processes involved in short-term memory. One, a “silent” process where the brain stores short-term memories without ongoing neural activity, and a second, more active process where circuits of neurons fire continuously.
The study, led by Nicholas Masse, Ph.D., a senior scientist at UChicago, and senior author David Freedman, Ph.D., professor of neurobiology, was published this week in Nature Neuroscience.
Continue reading “AI software reveals the inner workings of short-term memory” »
A group of researchers at Sandia National Laboratories have developed a tool that can cross-train standard convolutional neural networks (CNN) to a spiking neural model that can be used on neuromorphic processors. The researchers claim that the conversion will enable deep learning applications to take advantage of the much better energy efficiency of neuromorphic hardware, which are designed to mimic the way the biological neurons work.
The tool, known as Whetstone, works by adjusting artificial neuron behavior during the training phase to only activate when it reaches an appropriate threshold. As a result, neuron activation become a binary choice – either it spikes or it doesn’t. By doing so, Whetstone converts an artificial neural network into a spiking neural network. The tool does this by using an incremental “sharpening process” (hence Whetstone) through each network layer until the activation becomes discrete.
According to Whetstone researcher Brad Aimone, this discrete activation greatly minimizes communication costs between the layers, and thus energy consumption, but with only minimal loss of accuracy. “We continue to be impressed that without dramatically changing what the networks look like, we can get very close to a standard neural net [in accuracy],” he says. “We’re usually within a percent or so on performance.”
Continue reading “One Step Closer to Deep Learning on Neuromorphic Hardware” »
As living organisms eat, grow, and self-regenerate, all the while they are slowly dying. Chemically speaking, this is because life is thermodynamically unstable, while its ultimate waste products are in a state of thermal equilibrium. It’s somewhat of a morbid thought, but it’s also one of the characteristics that is common to all forms of life.
Now in a new study, researchers have created a self-replicator that self-assembles while simultaneously being destroyed. The synthetic system may help researchers better understand what separates biological matter from simpler chemical matter, and also how to create synthetic life in the lab.
The researchers, Ignacio Colomer, Sarah Morrow, and Stephen P. Fletcher, at the University of Oxford, have published a paper on the self-replicator in a recent issue of Nature Communications.
To answer the iconic question “Are We Alone?”, scientists around the world are also attempting to understand the origin of life. There are many pieces to the puzzle of how life began and many ways to put them together into a big picture. Some of the pieces are firmly established by the laws of chemistry and physics. Others are conjectures about what Earth was like four billion years ago, based on extrapolations of what we know from observing Earth today. However, there are still major gaps in our knowledge and these are necessarily filled in by best guesses.
We invited talented scientists to discuss their different opinions about the origin of life and the site of life’s origin. Most of them will agree that liquid water was necessary, but if we had a time machine and went back in time, would we find life first in a hydrothermal submarine setting in sea water or a fresh water site associated with emerging land masses?
“Thus, our data demonstrate the ability of multicellular organisms to survive long-term (tens of thousands of years) cryobiosis under the conditions of natural cryoconservation,” the researchers said in a study published in Doklady Biological Sciences.
