Other popular models involve duct tape and hamsters in wheels . . . and bacon.
Posted: Aug 6, 2012
The human brain is a big, complicated system, with different parts doing different things. No one fully understands how it works, yet. But like many other researchers, I think I have a fairly good idea, at a high level.
What I’m going to give you here is a capsule summary of how one AI researcher, and sometime computational neuroscientist, thinks about the structure and dynamics of the human brain — the “brain according to Ben,” if you will. I’ll also briefly discuss the relation between brain function and current approaches to artificial general intelligence.
If you want an in-depth yet concise tutorial on the current state of textbook neuroscience knowledge, try this one from Columbia University. Rather than reviewing the basics in “Neuroscience 101″ style detail, what I’m going to do here is give an overview of what I think are the most critical points and how they all fit together.
Reader beware, though: Neuroscientists have discovered a lot, but there are many different, widely divergent expert opinions about how to integrate the diverse data available from neuroscience into a coherent whole. The ideas I give here are just one opinion, albeit one I think is well grounded from a variety of directions.
The Big Picture
I like this picture created by IBM researcher Dharmendra Modha and his team:
As I discussed in an earlier blog post, this picture shows 300+ regions of the macaque monkey brain and how they connect to each other. Most of these correspond to similar regions in the human brain; and a similar diagram could be made for the human brain, but it would be less complete, as we’ve studied monkeys more thoroughly.
Each of these brain regions has a literature of scientific papers about it, telling you what sorts of functions they tend to carry out. In most cases, our knowledge of each brain region is badly incomplete. The nodes near the center of his diagram happen to correspond to what neuropsychologists call the “executive network” — the regions of the brain that tend to get active when the brain needs to control its overall activity.
But all these different parts of the brain do seem to work according to some common underlying principles. Each of them is wired together differently, but using the same sorts of parts; and there’s a lot of commonality to the dynamics occurring within each regions as well.
Between Neurons and Brains
All the parts of the brain are made of cells called neurons, that connect to each other and spread electricity amongst each other. The spread of electricity is mediated by chemicals called neurotransmitters — so, one neuron doesn’t simply spread electricity to another one, it activates certain neurotransmitter molecules that then deliver the charge to the other neuron. Things like mood or emotion or food or drugs affect these neurotransmitters, modulating the nature of thought.
There are also other cells in the brain, like the glia that fill up much of the space between neurons, that seem to play important roles in some kind of memory. Some folks have speculated that intelligence relies on complex quantum-physical phenomena occurring in water mega-molecules floating in between the neurons — though I have no idea if this is true or not.
The part of the brain most central for thinking and complex perception — as opposed to body movement or controlling the heard, etc. — is the cortex. And neurons in the cortex are generally organized into structures called columns. The column is the most critical structure occupying the intermediate level between neurons and the large-scale brain regions depicted in Modha’s diagram above. Each column spans the six layers of the cortex, passing charge up and down the layers and also laterally to other columns. There are a lot of neurons called “interneurons” that carry out inhibition between columns — when one column gets active, it sends charge to interneurons, that then inhibit the activity of certain other columns.
And columns tend to be divided into substructures that are often called “mini-columns”, or sometimes just “modules.” In some cases, it seems that each mini-column represents a certain pattern observed in some input, and the column as a whole represents a “belief” about which patterns are more significant in the input.
In the visual cortex, you can have columns recognizing particular patterns in particular regions of space-time, for instance. So one column might contain neurons responding to patterns in a particular part of the visual field — where the neurons higher up in the column represent more abstract, high-level patterns. Lower-level neurons in the column might recognize the edges of a car, whereas higher-level neurons in the same column might help identify that these edges, taken together, do actually look like car. But the functions of columns and the neurons and minicolumns inside them seem to vary a fair bit from one brain region to another.
If you’d like to dig deeper into the column/minicolumn aspect, check out this recent review of mini columns; and this more speculative paper, that proposes a particular function and circuitry for mini columns. A capsule summary of the literature these papers represent is:
* cortical columns are in many cases well-conceived as hierarchical pattern recognition units, using their minicolumns together to recognize patterns
* the minicolumns in various parts of cortex are implementing a variety of different sorts of microcircuitry, rather than possessing a uniform internal mini-columnar structure.
Read the whole essay.