> popular deep artificial neural networks (lstms, llms, etc.) are highly recurrent, in which they are simulating not deep networks, but shallow networks that pr

> popular deep artificial neural networks (lstms, llms, etc.) are highly recurrent, in which they are simulating not deep networks, but shallow networks that process information in loops many times. Thanks for the info. Is there anything you would recommend to dive deeper into this? Books/papers/courses/etc. > recommend not to oversimplify structure here. what you describing is only high-level structure of single part of brain (neocortex). Nice suggestion. I added a bit to make it clear that I'm talking about the neocortex. > 1 & 2 Totally. I don't think AI is a simple as building a Darwin Machine, much like it's not as simple as building a neural net. But I think the concept of a Darwin Machine is an interesting, and possibly important, component. My goal with this post was to introduce folks who hadn't heard of this concept and, hopefully, get in contact with folks who had. I left out the other so I could try to focus on what matters. > temporal dimension is important. your article is very ML-like focusing on information processing devoid of temporal dimension. if you want to draw parallels to real neurons in brain, need to explain how it fits into temporal dynamics (oscillations in neurons and circuits). Correct me if I misunderstand, but I believe I did. The spatio-temporal firing patterns of minicolumns contain the temporal dimension. I touched on the song analogy but we can go deeper here. Let's imagine the firing pattern of a minicolumn as a melody that fits within the period of some internal clock (I doubt there's actually a clock but I think it's a useful analogy). Each minicolumn starts "singing" its melody over and over, in time with the clock. Each clock cycle, every minicolumn is influenced by its neighbors within the network and they begin to sync up. Eventually they're all harmonizing to the same melody. A network might propagate a bunch of different melodies at once. When they meet, the melodies "compete". Each tries to propagate to a new minicolumn and fitness is judged by other inputs to that minicolumn (think sensory) and the tendencies of that minicolumn (think memory). I think the evolution is an incredible algorithm is because it relies as much as it does on time. > is this competition in realm of abeyant (what you can think in principle) or current (what you think now) representations? what's the timescales and neurological basis for this? I'm not familiar with these ideas but let me give it a shot. Feel free to jump in with more questions to help clarify. Neural Darwinism points to structures - minicolumns, cortical columns, and interesting features of their connections - and describes one possibility for how those structures might lead to thought. In your words, I think the structures are the realm of abeyant representations while the theory describes current representations. The neurological basis for this, the description of the abeyant representation (hope I'm getting that right), is Calvin's observations of the structure of the brain. Observations based on his and other's research. To a large extent, neuroscience doesn't have a great through-line-story of how the brain works. For example the idea of regions of the brain responsible for specific functions - like the hippocampus for memory - doesn't exactly play nice with Karl Lashley's experimental work on memory. What I liked most about this book is how Calvin tried to relate his theory to both structure and experimental results. > overall, my take it is a bit ML-like talk. if it describes real neurological networks it got to be closer and stronger neurological footing. If, by ML-like talk, you mean a bit woo-woo and hand wavy. Ya, I agree. Ideally I'd be a better writer. But I'm not, so I highly recommend the book. It's written by an incredible neuroscientist and, so far, none of the neuroscience researchers I've given it to have expressed anything other than excitement about it. And I explicitly told them to keep an eye out for places they might disagree. One of them is currently reading it a second time right now with the goal verifying everything. If it all checks out, he plans on presenting the ideas to his lab. I'll update the post if he, or anyone in his lab, finds something that doesn't check out. > here is some good material, if you want to dive into neuroscience. "Principles of Neurobiology", Liqun Luo, 2020 and "Fundamental Neuroscience", McGraw Hill. Why these two textbooks? I got my B.S. in neuroscience so I feel good about the foundations. Happy to check these out if you believe they add something that many other textbooks are missing.