Study Finds

Study: Internet, Human Brain Use Similar Algorithms to Process Info

DURHAM, N.C. — Do you know how the internet delivers information to your computer?  Turns out, it’s very similar to how our own minds process data. A new study finds that our brain and the internet use very similar algorithms or methods to manage the flow of information.

Scientists determined the internet uses an algorithm called “additive increase, multiplicative decrease” (AIMD) and brains use neuronal equivalents, known as long-term potentiation and long-term depression.

Similar rules govern the operation of the internet and our brains, a study finds.

Saket Navlakha of the Salk Institute for Biological Studies and Jonathan Y. Suen of Duke University co-authored this study, Using inspiration from synaptic plasticity rules to optimize traffic flow in distributed engineered networks, which was published online in the journal Neural Computation earlier this month.

According to the researchers, the internet was designed to move information along routes and avoid traffic jams. It does this through the AIMD algorithm, which was explained in a university release in this way:

…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.

The computer will continue to send data faster by integrals of one unit (additive increase) until it hits a delayed response, which means the route is congested. When it hits a delay, it reduces the speed by half—this is the multiplicative decrease part. The delay is so noticeable that it prompts computer users to pause and the system then catches up so “the whole system can continuously adjust to changing conditions, maximizing overall efficiency.”

Navlakha and Suen wanted to explore if the brain uses a similar system to process information traffic. So, they carried out mathematical modeling of neural activity. Using AIMD as well as six other flow-control algorithms, “they analyzed which model best matched physiological data on neural activity from 20 experimental studies,” and the result was: AIMD. AIMD was also found to be “the most efficient at keeping the flow of information moving smoothly” in the experiment.

Neurons in the brain transmit information throughout the body and to each other, and the contact points between them are called synapses. Neurons carry out a method of neuron firing called long-term potentiation, which the scientists compared to additive increase.

Long-term potentiation 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 is called long-term depression. This is when the neurons fire in reverse (the neuron that was previously first goes second), which weakens the connection between them. Much like a computer system, the entire neural system is continuously adapting and adjusting to these interactions.

“While the brain and the Internet clearly operate using very different mechanisms, both use simple local rules that give rise to global stability,” Suen says in the release. “I was initially surprised that biological neural networks utilized the same algorithms as their engineered counterparts, but, as we learned, the requirements for efficiency, robustness, and simplicity are common to both living organisms and the networks we have built.”

The authors point out that being able to model and better understand how neurons work in standard conditions may help us to be able to explore the neural disruptions that underlie some learning disabilities.

Much as a city’s highways and roads emulate a circulatory system, it seems that the founders of the internet created a way of regulating information flow that mimics the functioning of human physiology. “Discovering that the brain uses a similar algorithm may not be just a coincidence,” Navlakha said.

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