I have a question about the desired sparsity.
Well, typically we want 2%.
Now, in the SP algorithm (lets assume we have global inhibition) we have 2% of the columns active.
So lets look at an example:
total_numer_of_colmns = 2048
k = 40
cell_per_column = 4
total_number_of_cells = 2048 * 4 = 8192
Now we have 40 active columns in the layer. In the TM we activate at least 1 cell per column. Let’s assume for this example, that we have EXACTLY one cell per active column.
this would result in
number_of_active_cells = 40
Because we have a total number of cell of
8192, the overall sparsity of cells is:
overall_sparsity_of_cells = 0.48%
Now I’m not sure: In different papers it says, that the total number of active cells shouldn’t be to low, in order to recognize patterns reliably.
So my questions:
Q1 : Is the required total number of active cells meant in absolute term (so like 40 active cells instad of 2%)?
Q2: is there any disadvantage in just activating e.g. 0.48% of the cells? Or is it the opposite: Is it good for the false positive rate if we have a overall activity less than 2%?
Q3: How would the system be effected if I would choose k = 163 (163 is 2% of the total number of cells).
I would really appreciate some help.
Thank you all very much.