Explaining the brain with new plastic dynamical systems

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Photo by Hal Gatewood on Unsplash

Photo by Hal Gatewood on Unsplash

Why is it important?

The most burning problem in neuroscience has been the absence of a unifying brain theory, which could explicitly link the behavior and mental functions with the measurable brain parameters and incorporate current and future brain data. The biggest obstacle on the way to understanding the brain has been our inability to define key brain functions, such as intelligence, cognition and memory, in the language of natural sciences, and specifically of mathematics. All available definitions specify what these objects do or are able to do, but not what they are. For example, intelligence is often defined as mental ability for reasoning, problem solving, and learning, which is akin to the pre-scientific definition of electricity as property of attracting small objects after being rubbed. To handle intelligence scientifically, its definition(s) should be as clear-cut as modern definitions of electrical phenomena and should incorporate connections with experimentally observable quantities. Although the brain has long been modelled as a giant dynamical system, we argue that the conventional theory of dynamical systems is insufficient to explain intelligence and explain how it can be extended. To do this, we switch the focus from the traditional observable manifestations of intelligence, such as bodily movements and speech, Within out framework we propose mathematically explicit definitions for memory and cognition. Namely, memories are traces on the vector field of the brain, whereas cognition is the continual reshaping of this vector field affected by sensory stimuli.

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