Hypothetical scenario - two variables, one variable is exactly correlated with the target value and the other is completely random/uncorrelated values.

I use RDSE to encode both variables, so 2 x 400 bit (dimension) encodings each with 21 active_width (as per RDSE HTM school demo). They are concatenated to form the 800-bit input encoding

Is there a way for the Temporal Memory to “ignore” the unhelpful part of the encoding (the random variable)? Since, with global inhibition, the Spatial Pooler’s range is distributed across the entire input space, its cells are correlated with the activity of both the unhelpful half of the input space and the helpful half of the input space. Thus, the Temporal Memory cannot learn on the SP cells because the random variable “gets in the way” of the true correlated variable, constantly “mutating” their SP representation

Is there a way to automatically degrade the impact of this unhelpful half of the input space, or otherwise have the SP learn to ignore it? The idea being that the predictive performance converges to what it would be if only the exactly-correlated variable was in the input space