[ filtered-signal ] = [ pos[x1] ] [ 1 -1 ] [ signal ] [ off-current ]where pos[x] is the simplest of the sigmoids (and also corresponds to the fact that you can't have negative numbers of spikes):
def pos(x): if x <= 0: return 0 else: return xand:
signal is a time varying signal. off-current is a time varying off-current. (In this case an inhibitory signal of roughly the same strength as the signal) filtered-signal is the resultan example of a strongly inhibitory off-current:
[ filtered-signal ] = [ pos[x1] ] [ 1 -10 ] [ signal ] [ off-current ]an example of a weakly inhibitory off-current:
[ filtered-signal ] = [ pos[x1] ] [ 1 -0.2 ] [ signal ] [ off-current ]And now a BKO example:
M |yes> => |yes> + -1|no> M |no> => -1|yes> + |no> sa: matrix[M] [ no ] = [ 1 -1 ] [ no ] [ yes ] [ -1 1 ] [ yes ]Now some examples:
sa: drop M |yes> |yes> sa: drop M |no> |no> sa: drop M (|yes> + |no>) |> sa: drop M (0.8|yes> + 0.2|no>) 0.6|yes> sa: drop M (0.2|yes> + 0.8|no>) 0.6|no>All simple enough, and corresponds to the case when you have objects/concepts that are mutually exclusive.