2021 BRN Discussion, page-21495

Prof Wiki:
A cortical column, also called hypercolumn, macrocolumn,[1] functional column[2] or sometimes cortical module,[3] is a group of neurons in the cortex of the brain that can be successively penetrated by a probe inserted perpendicularly to the cortical surface, and which have nearly identical receptive fields.[citation needed] Neurons within a minicolumn (microcolumn) encode similar features, whereas a hypercolumn "denotes a unit containing a full set of values for any given set of receptive field parameters".[4] A cortical module is defined as either synonymous with a hypercolumn (Mountcastle) or as a tissue block of multiple overlapping hypercolumns.[5]The columnar hypothesis states that the cortex is composed of discrete, modular columns of neurons, characterized by a consistent connectivity profile.[2]It is still unclear what precisely is meant by the term, and it does not correspond to any single structure within the cortex. It has been impossible to find a canonical microcircuit that corresponds to the cortical column, and no genetic mechanism has been deciphered that designates how to construct a column.[4] However, the columnar organization hypothesis is currently the most widely adopted to explain the cortical processing of information.[6]

Non-neurophysicist's interpretation:

So it seems a cortical column contains:
Microcolumns - groups of closely related neurons encoding similar information;
Bundles of microcolumns wherein each of the microcolumns contains a differing version of information related to the microcolumns in the cortical column.

Does this mean that the neurons in a microcolumn have a small difference between other neurons in the particular microcolumn, and
the difference between information in the microcolumns is greater than the inter-microcolumn neuronal differences?

I guess that PvdM can implement this by configuring the weights of each neuron in a microcolumn, which could be done by one-shot/few-shot learning.