As far as I am aware, SDRs have only been discussed as binary arrays. That makes sense when you measure activity in a single moment. However if you measure activity over a period of time the SDRs could have a frequency. Related SDRs could be co-active during that period, but they are active at different frequencies. The SDR that represents the abstract concept of the show ‘Friends’ could co-activate a union of other SDRs relating to the characters such as Ross, Rachel or Monica. However, these SDRs could be graded, in that Rachel could have a higher activation frequency than Ross. This could simply be that Rachel is the most relevant character in Friends (… or at least it is for me…) so therefore has a higher activity. But there are bits that overlap between these SDRs, and those bits are more selective to Rachel, although they are shared across all characters.
It might be that synaptic integration zones are still weighted (based on the number of synapses between pre/post synaptic neurons) within the zone/segment. These weights are responsible for the differing frequencies among SDRs. If this is true then it might be possible to have graded response to a graded stimulus.
I was wondering, is there any interesting computation utility for graded SDRs?
EDIT: Instead of an SDR being represented as pure binary with indices [10,248,531,824,939,1262,1444,1782], it could be graded as integers where there is more than one occurrence of the same index [10,10,248,531,824,824,824,939,1262,1444,1444,1444,1444,1782] - a union of the same SDR over time. This will map out to {10:2 ,248:1 ,531:1 ,824:3, 939:1, 1262:1 ,1444:4 ,1782:1}