By now I've written a bunch of blog posts on brain architecture and algorithms, not in any particular order and generally interspersed with long digressions into Artificial General Intelligence. Here I want to summarize the key ideas in one place, to create a slightly better entry point, and something I can refer back to in certain future posts that I'm planning.

I am trying to paint a picture here, but this post won't really justify it, let alone prove it. Personally, my main justification is: I think I can basically understand how every piece can be implemented with biologically-plausible components, and I think I can basically understand how the pieces work together to explain absolutely everything in human cognition and psychology. (Try me!) Needless to say, I could be wrong in both the big picture and the details.

Pretty much everything I say here and its opposite can be found in the cognitive neuroscience literature. (It's a controversial field!) I make no pretense to originality but can't be bothered to put in actual references. Ask me if you're interested.

Let's start with a diagram, similar to the one I put in Inner Alignment in the Brain:

So let's start in:

Two subsystems: "Neocortex" (including thalamus, hippocampus, basal ganglia), and "Subcortex"

The neocortex is the home of "human intelligence" as we would recognize it—our beliefs, goals, ability to plan and learn and understand, every aspect of our conscious awareness, etc. etc. (All mammals have a neocortex; birds and lizards have an analogous structure called the "pallium".) Some other parts of the brain (thalamus, hippocampus, basal ganglia) help the neocortex do its calculations, and I lump them into the neocortex subsystem. I'll use the term subcortex for the rest of the brain (midbrain, amygdala, etc.).

Cortical uniformity

The neocortex is, to a first approximation, architecturally uniform, i.e. all parts of it are running the same generic learning algorithm in a highly-parallelized way. The caveats (spelled out in more detail at the link) are:

There are sorta "hyperparameters" on the generic learning algorithm which are set differently in different parts of the neocortex—for example, different regions have different densities of each neuron type, different thresholds for making new connections (which also depend on age), etc.
There is a gross wiring diagram hardcoded in the genome—i.e., set of connections between different neocortical regions and each other, and other parts of the brain. These connections later get refined and edited during learning. These speed the learning process by bringing together information streams with learnable relationships—for example the wiring diagram seeds strong connections between toe-related motor output areas and toe-related proprioceptive (body position sense) input areas.

What is the neocortical algorithm?

"Analysis by synthesis" + "Planning by probabilistic inference": "Analysis by synthesis" means that the neocortex searches through a space of generative models for a model that predicts its upcoming inputs (both external inputs, like vision, and internal inputs, like proprioception and reward). "Planning by probabilistic inference" (term from here) means that we treat our own actions as probabilistic variables to be modeled, just like everything else. In other words, the neocortex's output lines (motor outputs, hormone outputs, etc.) are the same type of signal as any generative model prediction, and processed in the same way........... g As shown in this figure, the neocortex builds a world-model in the form of collection ("society") of generative models, and it continually searches through this collection for models that make correct predictions, and favoring those models which predict future reward. in other words, you are treating your own future actions as probabilistic variables to be modeled just like any other part of the universe. We take adaptive actions only because, as mentioned above, we favor generative models that predict we will get rewarded. (I take the term "planning by probabilistic inference" from this non-brain-related paper.)

All of these are slight oversimplifications, and I won't justify here why I believe all these things, except to say that I'm really loving this framework so far. I feel like I can relate it to both lower levels (i.e. how each component is implemented) and higher levels (i.e. how human behavior comes out of it), and everything fits together beautifully.