So, in my work I use “superpositions”, which can be considerd a type of float SDR’s. They have most of the properties of regular SDR’s, including a similarity measure. I think most of the time the brain probably does use just binary SDR’s, but in a few cases perhaps not? eg, with binary how do you represent “a little”, “somewhat” and “very”, or probabilities for that matter?
For example, in my notation you would represent “very hungry and a little tired” as:
0.9|hungry> + 0.1|tired>
Another example, is Schrodinger’s cat alive or dead? In my notation the 50/50 probability would be represented by:
0.5|alive> + 0.5|dead>
Exactly how the above couple of examples map to the brain I don’t know, and unlike HTM, my focus isn’t strict adherence to the biology/neuroscience. Heh, we all need our own niche. First, the “kets” |hungry>, |tired>, |alive> and |dead> I assume correspond to specific neurons that represent that concept. As for the coefficients, perhaps they correspond to a simple sum of spikes during some time window. In any case, whatever the mapping, I have found float SDR’s to be useful.
BTW, if you want superpositions to look more like standard binary SDR’s, consider superpositions such as:
|273> + |186> + |1897> + |314> + |453> + |49> + |332> + |1461> + |1740> + |159>
which is this binary SDR (ie, list of on bits):
[273, 186, 1897, 314, 453, 49, 332, 1461, 1740, 159]
Anyway, that is my 2c.
See here for example:
forum post
code