Anyway, words are boring, lets give an example in the console:

-- load up this data: sa: dump ---------------------------------------- |context> => |context: non-linear resonance> non-linear-resonance |*> #=> 1000 drop-below[0.99] simm(""|_self>, ""|g>) |g> weak-resonance |*> #=> 200 drop-below[0.6] simm(""|_self>, ""|g>) |g> |g> => |a> + |b> + |c> + |d> |f1> => |a> |f2> => |a> + |b> |f3> => |a> + |b> + |c> |f4> => |a> + |b> + |c> + 0.9|d> |f5> => 0.95|a> + |b> + |c> + |d> |f6> => |a> + |b> + |c> + |d> |f7> => |a> + |b> + |c> + |d> + |e> |list> => |f1> + |f2> + |f3> + |f4> + |f5> + |f6> + |f7> ----------------------------------------where g is our incoming pattern, and f_k are our stored patterns.

And we have defined our weak-resonance and non-linear-resonance operators (where we need at least 60% similarity for the weak-resonance, and 99% similarity for the non-linear-resonance, and the amplitude of the non-linear-resonance is much higher).

And then, let's look at the resulting table:

sa: table[pattern,weak-resonance,non-linear-resonance] "" |list> +---------+----------------+----------------------+ | pattern | weak-resonance | non-linear-resonance | +---------+----------------+----------------------+ | f1 | | | | f2 | | | | f3 | 150 g | | | f4 | 196.15 g | | | f5 | 198.10 g | 990.51 g | | f6 | 200 g | 1000 g | | f7 | 160 g | | +---------+----------------+----------------------+I'm not sure how that table looks to others, but to me it is very, very beautiful. It is showing hints of stuff I have been thinking about for a long, long time now.

Anyway, some comments:

1) the patterns can of course be anything. eg, a very specific sequence of sounds could non-linear resonate with the "frog" neuron. The specific sequence of letters for "beach" might weak-resonate with the "sun", "sand", "waves" and "beach-goers" neurons.

2) the above of course has a lot of similarity with the similar[op] operator.

3) I suspect something very similar to that table happens in the hippocampus. But that is for later!

Update: the above is essentially a 1-D version of the landscape function:

L(f,x) = simm(f,g(x))

Though here we have f and g swapped, so it is:

L(g,x) = simm(g,f(x))

Update: on reflection, I don't think the "beach" thing is a good example of a weak resonance. I'll have to see if I can think of a better example.

Update: we can also have a "square resonance":

square-resonance |*> #=> 200 clean drop-below[0.6] simm(""|_self>, ""|g>) |g>Now, look at the table:

sa: table[pattern,weak-resonance,square-resonance,non-linear-resonance] "" |list> +---------+----------------+------------------+----------------------+ | pattern | weak-resonance | square-resonance | non-linear-resonance | +---------+----------------+------------------+----------------------+ | f1 | | | | | f2 | | | | | f3 | 150 g | 200 g | | | f4 | 196.15 g | 200 g | | | f5 | 198.10 g | 200 g | 990.51 g | | f6 | 200 g | 200 g | 1000 g | | f7 | 160 g | 200 g | | +---------+----------------+------------------+----------------------+Anyway, that should be clear enough. And of course, we can make (almost) arbitrary permutations of the shape of the resonance.

Also, we could call "weak resonance" a "fuzzy resonance". Ie, the pattern only has to be fuzzy close, yet it still resonates.

Home

previous: wikipedia fragment to frequency list

next: some more similar inverse links to results

updated: 19/12/2016

by Garry Morrison

email: garry -at- semantic-db.org