Grand Unified Theory of the Brain?

While I get nervous when the term “machine” or “computer” is used to describe the human brain, progress in explaining how the brain works is necessary.

To me, there’s more to it than “how a computer works” or “how a machine processes things” because what describes what we term as irrational behavior?

If someone were to answer that with “computer virus” … I would ask “Then where is the anti-virus ‘software’?” Since the answers to those questions are not as simple as reducing the brain down to simple logic … I’m really hesitant to use “computer” or “machine” to describe the brain.

That said, I’m also aware of what we have discovered, and can see where overall mechanisms of the brain could be described as such, just not the totality.

Because describing the brain “as a” computer or machine in totality is too simplistic, too analogous. These terms limit us to boiling down the description of the workings of the brain to that of a robotic – type device (not robot, I’m using analogy too).

I just think there is way more to our brains. The “general relativity” that will describe our brains will be far more complex than what seems to be described in these recent papers (in my opinion).

But we will see.

Techno Occulture

Neuroscientist Karl Friston and his colleagues have proposed a mathematical law that some are claiming is the nearest thing yet to a grand unified theory of the brain.
………– Stanislas Dehaene

Carrying on a conversation with my friend Scott Bakker of Three-Pound Brain led him to mention Stanislas Dehaene. Dehaene a couple years back had mentioned the work of Karl Friston who may be closer than anyone else to providing a solid framework for the neurosciences going forward. In his paper on the free energy principle (here: A free energy principle for the brain: pdf) he mentions the basics of this concept:

By formulating Helmholtz’s ideas about perception, in terms of modern-day theories, one arrives at a model of perceptual inference and learning that can explain a remarkable range of neurobiological facts: using constructs from statistical physics, the problems of inferring the causes of sensory input and learning…

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