Self adjusting receptive fields

Real brains are highly “plastic”; regions of the neocortex can learn to represent entirely different things in reaction to various changes. If part of the neocortex is damaged, other parts will adjust to represent what the damaged part used to represent. If a sensory organ is damaged or changed, the associated part of the neocortex will adjust to represent something else. The system is self-adjusting.

We want our HTM regions to exhibit the same flexibility. If we allocate 10,000 columns to a region, it should learn how to best represent the input with 10,000 columns. If we allocate 20,000 columns, it should learn how best to use that number. If the input statistics change, the columns should change to best represent the new reality. In short, the designer of an HTM should be able to allocate any resources to a region and the region will do the best job it can of representing the input based on the available columns and input statistics. The general rule is that

with more columns in a region, each column will represent larger and more detailed patterns in the input. Typically the columns also will be active less often, yet we will

maintain a relative constant sparsity level.

No new learning rules are required to achieve this highly desirable goal. By boosting inactive columns, inhibiting neighboring columns to maintain constant sparsity, establishing minimal thresholds for input, maintaining a large pool of potential synapses, and adding and forgetting synapses based on their contribution, the ensemble of columns will dynamically configure to achieve the desired effect.