Hey everybody,

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.

Helena