Current implementations of the “location layer” work as a collection of grid cell modules where one or more bumps of activity are essentially “pushed around” as the location being represented changes. When the object and location being sensed is unambiguous, this means one active cell per module.
However, this seems to contradict (or at least not fully implement) other observations about the system related to encoding context by use of minicolumns, and I am having some trouble reconciling the ideas in my mind. I thought I would start a thread to get some insights from other HTM theorists.
One example where minicolumns are implied to be part of a location layer, is in part 1 of the podcast (around 26:00). Jeff talked in some length about applying to locations what has been learned from TM (with respect to minicolumns to encode context). A couple of relevant quotes from that discussion:
So, the point is, this idea that you represent or you have something, like – we started by talking about the sensory input, and I want to represent the sensory input in different contexts. I now have a location on an object, but I want to represent that location under different contexts. Because if I’m going to predict what’s going to be at that location, I need to know the context, or the state, of the cell phone or the state of the stapler.
So this basic idea … this is happening everywhere, that you represent something, like a sensory input or like a location … and I want to be able to represent it under many different contexts. And so that’s where the role of minicolumns comes into play.
The idea of minicolumns being part of the location system also seems to be implied in the Frameworks paper as well. A couple of relevant quotes:
As the stapler top rotates upward, the displacement of the stapler top to bottom changes. Thus, the rotation of the stapler top is represented by a sequence of displacement vectors. By learning this sequence, the system will have learned this behavior of the object.
Opening and closing the stapler are different behaviors yet they are composed of the same displacement elements, just in reverse order. These are sometimes referred to as “high-order” sequences. Previously we described a neural mechanism for learning high-order sequences in a layer of neurons (Hawkins & Ahmad, 2016).
This is of course a reference to minicolumns, which are how HTM learns high order sequences.
Note that I am not necessarily implying the existence or functional relevance of cells arranged into physical minicolumns (Jeff has made this point before as well), but rather the underlying function (which is easier to visualize as arrangements of minicolumns).
In my current (perhaps limited) understanding, the “minicolumn effect” should be a basic, inherent property of a neural network where cells have a proximal receptive field close to the cell body, and several are positioned near enough together that they share similar enough receptive fields and can compete to inhibit each-other.
In other words, should the bumps of activity in the GCM actually represent more than one cell sharing a common receptive field, thus allowing a TM-like algorithm to be applied for representing context?