The pursuit of strong artificial intelligence involves numerous areas of research. While a lot of current research in AI is focused on achieving various intelligent tasks that the brain is capable of, disciplines like computational neuroscience seek to understand how the brain works and achieves certain tasks in general. In a recent study from the field, researchers at MIT have been able to model how the mind is able to learn new things, known as neuroplasticity, while still retaining old things it's learned.
The research has shown that neurons are constantly trying out new configurations of how they connect to other neurons to allow for the brain to learn as many tasks as it needs to and find the best configuration. This allows neurons to specialize in certain tasks while others are still able to learn new tasks.
One key element in the study that was as yet not widely explored was determining how noise acts within the model. The researchers found that noise could actually benefit the model by exploring more new connection configurations when the model is hyperplastic. The researchers concluded that the noise actually helped the model learn a variety of new things while retaining the ability to do old rather than hindering it. The model also helps explain how skills can diminish when not practiced often enough since the new connections will eventually start to overwrite old skills after too much time has elapsed.
Only time will tell if this research will lead to further research and findings on the subject or just remain an interesting fact. Regardless, any breakthrough such as this in computational neuroscience helps towards both our understanding of how the human brain works in general as well as the long term goal of trying to create an intelligence on par with it.
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