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Study finds similar algorithm governing the brain and Internet

Although we spend a lot of our time online nowadays–streaming music and video, checking email and social media, or obsessively reading the news–few of us know about the mathematical algorithms that manage how our content is delivered. But deciding how to route information fairly and efficiently through a distributed system with no central authority was a priority for the Internet’s founders. Now, a Salk Institute discovery shows that an algorithm used for the Internet is also at work in the human brain, an insight that improves our understanding of engineered and neural networks and potentially even learning disabilities.

“The founders of the Internet spent a lot of time considering how to make information flow efficiently,” says Salk Assistant Professor Saket Navlakha, coauthor of the new study that appears online in Neural Computation on February 9, 2017. “Finding that an engineered system and an evolved biological one arise at a similar solution to a problem is really interesting.”

In the engineered system, the solution involves controlling information flow such that routes are neither clogged nor underutilized by checking how congested the Internet is. To accomplish this, the Internet employs an algorithm called “additive increase, multiplicative decrease” (AIMD) in which your computer sends a packet of data and then listens for an acknowledgement from the receiver: If the packet is promptly acknowledged, the network is not overloaded and your data can be transmitted through the network at a higher rate. With each successive successful packet, your computer knows it’s safe to increase its speed by one unit, which is the additive increase part. But if an acknowledgement is delayed or lost your computer knows that there is congestion and slows down by a large amount, such as by half, which is the multiplicative decrease part. In this way, users gradually find their “sweet spot,” and congestion is avoided because users take their foot off the gas, so to speak, as soon as they notice a slowdown. As computers throughout the network utilize this strategy, the whole system can continuously adjust to changing conditions, maximizing overall efficiency.

Navlakha, who develops algorithms to understand complex biological networks, wondered if the brain, with its billions of distributed neurons, was managing information similarly. So, he and coauthor Jonathan Suen, a postdoctoral scholar at Duke University, set out to mathematically model neural activity.

Because AIMD is one of a number of flow-control algorithms, the duo decided to model six others as well. In addition, they analyzed which model best matched physiological data on neural activity from 20 experimental studies. In their models, AIMD turned out to be the most efficient at keeping the flow of information moving smoothly, adjusting traffic rates whenever paths got too congested. More interestingly, AIMD also turned out to best explain what was happening to neurons experimentally.

It turns out the neuronal equivalent of additive increase is called long-term potentiation. It occurs when one neuron fires closely after another, which strengthens their synaptic connection and makes it slightly more likely the first will trigger the second in the future. The neuronal equivalent of multiplicative decrease occurs when the firing of two neurons is reversed (second before first), which weakens their connection, making the first much less likely to trigger the second in the future. This is called long-term depression. As synapses throughout the network weaken or strengthen according to this rule, the whole system adapts and learns.

When synapses get stronger and more effective they also become bigger, and conversely they shrink when they weaken. Thus, Cirelli and Tononi reasoned that a direct test of SHY was to determine whether the size of synapses changes between sleep and wake. To do so, they used a method with extremely high spatial resolution called serial scanning 3-D electron microscopy.

 

Ohio State University researchers help improve safety of child car seats’ installation

Many parents roll up towels and blankets and use pool noodles just to get their child’s car seat to fit better in their car. This common practice creates extra steps for parents and can make proper installation more difficult.

It turns out child car seats and vehicle seats don’t align properly more than 40 percent of the time, according to a new study.

In an effort to improve child car seats’ fit and position in vehicles, a team of researchers from The Ohio State University College of Medicine collected dimension samples from 61 vehicles and 59 child car seats currently on the market and identified the most common sources of incompatibility.

Data from nearly 3,600 potential child car seat-vehicle combinations and 34 physical installations were analyzed.

The results, which will be published in the journal Traffic Injury Prevention in early October, showed less than 60 percent of rear-facing child car seat-vehicle combinations fit properly between the vehicle’s seat pan angle and the child car seat manufacturer’s required base angle.

“I want to emphasize that all car seats are safe and have passed federal regulations. But, to really optimize the safety of a child’s car seat and provide the best protection for the child, one must make sure it fits properly in the vehicle,” said Julie Bing, lead author of the study and research engineer at Ohio State College of Medicine’s Injury Biomechanics Research Center.

Researchers found: the width of the base of child car seats fit snugly between the vehicle’s seat pan bolsters in more than 63 percent of rear-facing child car seat-vehicle combinations and in more than 62 percent of forward-facing child car seat-vehicle combinations; forward-facing child car seats didn’t bump up against vehicle’s headrests in more than 66 percent of the combinations; and compatibility rates of the length of the child car seat base compared to the length of the vehicle seat pan and the ability of the top tether to reach the tether anchor exceeded 98 percent.