> I think this is over-simplified and possibly misunderstood.

I'm with you here. I wrote this because I wanted to drive people towards the book. It's incredible and I did it little justice.

> "cortical activity produces spatial patterns which somehow 'compete' and the 'winner' is chosen which is then reinforced through a 'reward'"

A slight modification: spatio-temporal patterns*. Otherwise you're dead on.

> 'Compete', 'winner', and 'reward' are all left undefined in the article.

You're right. I left these undefined because I don't believe I have a firm understanding of how they work. Here's some speculation that might help clarify.

Compete - The field of minicolumns is an environment. A spatio-temporal pattern "survives" when a minicolumn is firing in that pattern. It's "fit" if it's able to effectively spread to other minicolumns. Eventually, as different firing patterns spread across the surface area of the neocortex, a border will form between two distinct firing patterns. They "Compete" insofar as each firing pattern tries to "convert" minicolumns to fire in their specific pattern instead of another.

Winner - This has two levels. First, an individual firing pattern could "win" the competition by spreading to a new minicolumn. Second, amalgamations of firing patterns, the overall firing pattern of a cortical column, could match reality better than others. This is a very hand-wavy answer, because I have no intuition for how this might happen. At a high level, the winning thought is likely the one that best matches perception. How this works seems like a bit of a paradox as these thoughts are perception. I suspect this is done through prediction. E.g. "If that person is my grandmother, she'll probably smile and call my name". Again, super hand-wavy, questions like this are why I posted this hoping to get in touch with people who have spent more time studying this.

Reward - I'm an interested amateur when it comes to ML, and folks have been great about pointing out areas that I should go deeper. I have only a basic understanding of how reward functions work. I imagine the minicolumns as small neural networks and alluded to "reward" in the same sense. I have no idea what that reward algorithm is or if NNs are even a good analogy. Again, I really recommend the book if you're interested in a deeper explanation of this.

> the theory is not new information and seems incredibly analogous to Hebbian learning which is a long-standing theory in neuroscience.

I disagree with you here. Hebbian learning is very much a component of this theory, but not the whole. The last two constraints were inspired by it and, in hindsight, I should have been more explicit about that. But, Hebbian learning describes a tendency to average, "cells that fire together wire together". Please feel free to push back here but, the concept of Darwin Machines fits the constraints of Hebbian learning while still offering a seemingly valid description of how creative thought might occur. Something that, if I'm not misunderstanding, is undoubtedly new information.

> I don't see any evidence that the brain will always produce several candidate activity patterns before judging a winner based on consensus.

That's probably my fault in the retelling, check out the book: http://williamcalvin.com/bk9/index.htm

I think if you read Chapters 1-4 (about 60 pages and with plenty of awesome diagrams) you'd have a sense for why Calvin believes this (whether you agree or not would be a fun conversation).

> The tangent of cortical columns ignores key deep brain structures and is also almost irrelevant, the brain could use the proposed 'evolutionary' process with any architecture.

I disagree here. A common mistake I think we to make is assuming evolution and natural selection are equivalent. Some examples of natural selection: A diversified portfolio, or a beach with large grains of sand due to some intricacy of the currents. Dawkinsian evolution is much much rarer. I can only think of three examples of architectures that have pulled it off. Genes, and their architecture, are one. Memes (imitated behavior) are another. Many animals imitate, but only one species has been able to build architecture to allow those behaviors to undergo an evolutionary process. Humans. And finally, if this theory is right, spatiotemporal patterns and the columnar architecture of the brain is the third.

Ignoring Darwin Machines, there are only two architectures that have led to an evolutionary process. Saying we could use "any architecture" seems a bit optimistic.

I appreciate the thoughtful response.