The cortical column, a vertically arranged group of interconnected neurons, is considered by many to be the fundamental functional module of the brain's neocortex. The human cortex is estimated to contain as many as two million columns each having between 50,000 and 100,000 neurons. A few months ago, while reading about the Blue Brain Project (BBP), it occurred to me that nobody has come up with a functional model of the cortical column that makes much sense. What I mean is that, sure there are a few theories and models floating around, but nobody really understands the purpose of the column. Since cortical columns are the main constituents of the brain's grey matter, a correct understanding would go a long way toward solving the intelligence puzzle. I began to think about the problem in light of what I have uncovered so far in my own AI research. In this article, I introduce rebel column, a new model of the human cortical column.
Simulating Without Understanding at the École Polytechnique Fédérale de Lausanne
BBP researchers claimed to have succeeded in using a super computer to simulate the cortical column of the rat. If true, it should have been heralded as the greatest advance in the history of neuroscience and artificial intelligence research. After all, to understand the cortical column is to understand a huge chunk of the brain. This clearly is not the case here. Henry Markram, the Blue Brain project director at the École Polytechnique Fédérale de Lausanne in Switzerland, would be hard pressed to describe the actual purpose or function of a column. Other than the usual "it is the functional building block of the brain's neocortex", BBP researchers have diddly squat to say about what it does and why. How does one know whether or not a simulation is working properly if one has no idea what it is supposed to do and why?
So after spending millions of dollars and six years trying to simulate the cortical column, the brightest brains in the field are none the wiser. This is not very promising, in my opinion. And for them to call the project a success is quite an exaggeration, to put it mildly. Lame.
Note: I am not a neuroscientist. I am a Christian researcher and I get my understanding of the brain by decoding certain ancient Biblical metaphorical texts. If this bothers you, please ignore my blog as it is not meant for you. I only write for kindred spirits, sorry. The Rebel Column hypothesis described in this article came from my own research which is not based on existing neuroscience literature. This is my way of establishing prior art (you read it here first). Please make copies. The Rebel Cortex document (pdf) has not yet been updated with this information.Definitions
A rebel column is several things in one. It is primarily a sensorimotor unit but it is also a sequence learner and recognizer, and an event recorder and predictor. Every column is organized around a single input event (the main event) which, depending on the column's position in the memory hierarchy, can be either a pattern or a sequence of patterns. Here, a pattern is defined as a group of concurrent signals originating from the sensory layer. While the motor function of the cortical column is beyond the scope of this article, suffice it to say that an event can also serve as a motor command. In a future article, I'll explain how patterns are formed and how the brain coordinates motor commands.
Columns, Minicolumns, Timing, Sequences and Memory Traces
A rebel column represents a single sequence of up to seven unique events. Why seven? Because seven is sufficient to identify a sequence. (Note that a sequence can have more than seven nodes but only the first seven are used to build the sequence hierarchy.) Knowing this, however, does not explain the apparent neural complexity of the column. What makes a column complex is that the timing of the events in the sequence can vary greatly. Recognizing a sequence of events does not consist of merely detecting their order of arrival but also their precise relative timing. The column accomplishes this feat by using a small population of winner-take-all minicolumns. I say winner-take-all because there can only be a single active detection at any one time. This explains why minicolumns send out inhibitory connections to all their neighbors within a given column. In other words, as soon as a minicolumn recognizes a sequence, it sends a signal to all the other minicolumns to prevent them from activating. The illustration below represents a small horizontal cross section of the neocortex showing nine hypothetical columns, each consisting of 11 minicolumns (small circles). The currently active minicolumns are shown in blue.
Why use multiple minicolumns? Why not have a single minicolumn and use multiple synaptic connections to represent the intervals between events? There are several reasons, in my opinion, having to do with recognition speed, event recording and recall, and prediction.
- A minicolumn can very quickly recognize a particular sequence if the events arrive at their precise expected times.
- If a given sequence is recognized by a minicolumn, the latter can serve as the memory trace for that sequence. If necessary, the column can be checked later to determine which minicolumn fired last.
- The timing of a sequence is represented by a single minicolumn. This way, the brain can quickly judge or predict the timing of following sequences (other columns) based on which minicolumn fired last.
In Part II, I will go over the sequence learning mechanism of a rebel column, how columns fit within the memory hierarchy and the Biblical metaphors that I used in my research. Hang in there.