Researchers have found evidence of an algorithm extensively used on the Internet being on work in human brain indicating that our brain and the Internet are more alike.
Pointing out at how information is relayed to the users over the Internet from servers distributed across the world, researchers say that information flow on the Internet is efficient and a similar instance of algorithm is at works in our brain as well. Researchers at Salk Institute say that the insight they have garnered through their finding could improve our understanding of engineered and neural networks and potentially even learning disabilities.
To ensure that communications on the Internet are efficient and unclogged, an algorithm called “additive increase, multiplicative decrease” (AIMD) is being used. In this algorithm, 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.
Because AIMD is one of a number of flow-control algorithms, researchers 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.
Understanding how the system works under normal conditions could help neuroscientists better understand what happens when these results are disrupted, for example, in learning disabilities.