simm in the matsumsig model
So, we have basics like simple logic, and set union and intersection in the MatSumSig model. Interestingly, we have a version of simm too! ie, simm becomes somewhat biologically plausible. Heh, but to tell the truth, I don't care if the brain doesn't actually use simm, since I have found it to be very useful.
Anyway, recall one definition of simm:
simm(w,f,g) = \Sum_k w[k] min(f[k],g[k]) / max(w*f,w*g)
If we ignore the max(w*f,w*g) denominator, here is a MatSumSig version of simm:
[ r ] = [ sigmoid[x1] ] [ w1 w2 w3 w4 ] [ pos[x1] ] [ 1 -1 -1 0 0 0 0 0 0 0 0 0 ] [ pos[x1] ] [ 1 1 0 0 0 0 0 0 ] [ f1 ]
[ pos[x2] ] [ 0 0 0 1 -1 -1 0 0 0 0 0 0 ] [ pos[x2] ] [ 1 -1 0 0 0 0 0 0 ] [ g1 ]
[ pos[x3] ] [ 0 0 0 0 0 0 1 -1 -1 0 0 0 ] [ pos[x3] ] [ -1 1 0 0 0 0 0 0 ] [ f2 ]
[ pos[x4] ] [ 0 0 0 0 0 0 0 0 0 1 -1 -1 ] [ pos[x4] ] [ 0 0 1 1 0 0 0 0 ] [ g2 ]
[ pos[x5] ] [ 0 0 1 -1 0 0 0 0 ] [ f3 ]
[ pos[x6] ] [ 0 0 -1 1 0 0 0 0 ] [ g3 ]
[ pos[x7] ] [ 0 0 0 0 1 1 0 0 ] [ f4 ]
[ pos[x8] ] [ 0 0 0 0 1 -1 0 0 ] [ g4 ]
[ pos[x9] ] [ 0 0 0 0 -1 1 0 0 ]
[ pos[x10] ] [ 0 0 0 0 0 0 1 1 ]
[ pos[x11] ] [ 0 0 0 0 0 0 1 -1 ]
[ pos[x12] ] [ 0 0 0 0 0 0 -1 1 ]
where it is assumed w_k >= 0.
If we extract out the intersection component, see last post, we have:
[I1,I2,I3,I4] = 2* [min(f1,g1), min(f2,g2), min(f3,g3), min(f4,g4)]
[ r ] = [ sigmoid[x1] ] [ w1 w2 w3 w4 ] [ I1 ]
[ I2 ]
[ I3 ]
[ I4 ]
Now, the above can be considered a space based simm. We can also do a time based one. I think it goes like this, though I haven't given this much thought in a long, long time!
[ r ] = [ sum[x1,t2] ] [ sigmoid[x1,t1] ] [ 1 -1 -1 ] [ pos[x1] ] [ 1 1 ] [ f ]
[ pos[x2] ] [ 1 -1 ] [ g ]
[ pos[x3] ] [ -1 1 ]
where [ sum[x1,t2] ] is the time based equivalent of [ w1 w2 w3 w4 ]
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updated: 19/12/2016
by Garry Morrison
email: garry -at- semantic-db.org