The internet and our brains are very much alike in today’s world, more so alike than people think. In society today we spend a great deal of our time online. Whether it is shopping, leisure, or reading the news. Yet many people do not stop and think how we understand information so effectively and fairly easy. Recent studies have shown that there are key mathematical algorithms that manage how our content is delivered to us. Included in these studies, certain instances were brought forth that there may be a certain 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.
In most engineered systems solutions include controlling the amount of information flow so the different routes become 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 (ACK) 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. As each packet is successfully sent, the computer knows to it is fine to keep sending packets. With that being said with every lose or drop of a packet, the computer knows that there is congestion and slows down significantly. In relation to the human brain, it turns out the neuronal equivalent of additive increase is what is known as long-term potentiate. 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, which weakens their connection, making the first much less likely to trigger the second. Understanding how the system works under normal conditions could help neurosciences better understand what happens when these results are disrupted, for example, in learning disabilities. All in all, it is a very extensive topic. If you like what you’ve read share and follow me on Facebook to read more and similar stories.