From: hsr4@vax.ox.ac.uk (Auld Sprurklie)
Newsgroups: sci.nanotech
Subject: Re: Multiple Experiences
Date: 21 Aug 92 22:27:44 GMT

In article <Aug.19.20.18.58.1992.28197@planchet.rutgers.edu>, sinster@cse.ucsc.edu (Darren Senn) writes:
> In article <Aug.15.10.19.21.1992.10779@planchet.rutgers.edu> hsr4@vax.ox.ac.uk (Auld Sprurklie) writes:
""From what I'd read so far, it appeared to me that the memories -are-
""associated with the means used to store them (don't different transfer
""functions apply to storage of different types of memory, some transfer
""functions being unsuitable for learning in certain cases ?), [...]
" 
" The transfer function is generally fixed when the network is first
" created: it's not learned.  So the transfer function is really part of
" the net's structure, not the memories.  Certainly, the exact values of
" the weights and thresholds depend on the network's transfer functions,
" but since only the weights and thresholds change, it's somewhat
" meaningless to associate the memories (a purely learned phenomenon)
" with the transfer functions (a static phenomenon).

I know that at present the transfer function is fixed at creation, but I'll
have to go back and re-read my references; my understanding was that different
transfer functions were suited to different applications - else why have
different transfer functions; surely one would suffice ?  If the function
introduces non-linearities into the network in order to facilitate the learning
of information (the creation of memories), then I would assume that while the
function is associated with the network structure it is also related, however
obscurely, to the type(s) of memory being created.

Or am I way off beam ?

[bits deleted]

" So in order to copy a whole skill, you must discover all the memories
" that compose that skill and its subskills, and teach those memories to
" the recipient network.  Once you have the memory, teaching it is simple.
" Finding the memories that compose the skill is a different matter
" entirely.  :(

Yes, that was one of my points earlier - how do you determine which memories
or skills lie where, and how do you obtain them if everything is inter-
dependent ?  In addition, if you need to update an existing skill (perhaps one
which was incompletely learned at an earlier age) how best to install that new
version of the skill so that it fits seamlessly ?

" [...]
""[...]  The obvious
""question which follows on is: how do you decide which dimple or group
""of dimples corresponds to a particular skill/memory ?  And if we still
""take the parallel dump from clones to primary as a possibility, would
""it be possible to differentiate the new skills/memories from the set
""supplied to form the clone(s) in the first place ?
" 
" Detecting which dimple/group corresponds to a particular memory is simple.
" Assert the memory onto the network (that means provide the network's
" input _and_ output) and calculate the optimal set of weights for that
" memory (there's a generalized formula for this).  Euclidean distance
[...]

I think I wondered in an earlier post about the necessity for presenting an
original input to a network in order to elicit its whereabouts (although that
was for the purpose of 'forgetting' it in order to replace it).

[...]

" The danger, when teaching a new memory to a network, is that old memories
" might be forgotten, or just become harder to reach.  Imaging a stretched
" sheet of silk with a number of marbles glued to certain positions.  These
" marbles will deform the silk.  If you drop a glass bead, somewhere, it'll
" roll around until it runs into a marble.  This marble is the memory
" associated with the input corresponding to the bead's original placement.
" If you put a bowling ball on this sheet of silk, then a large number of
" marbles will never be hit by the bead.  The bowling ball represents a
" memory that is remarkably easy for a particular network to learn, or
" a memory that has a large input space (imagine teaching a network to
" recognize individual faces -- the marbles -- and then training that same
" net to recognize Caucasians -- the bowling ball).

By large input space, I assume you mean having been presented much more often
than other inputs (such as the ability to recognise faces the right way up,
but not when oriented upside down - since they are not often encountered in
that orientation).

This may be a blind alley, but I seem to recall that the older one becomes,
the easier it appears to be to recall earlier memories - as if they are
coming to the surface (poor analogy, I know).  Perhaps the ability to be
cloned successfully will be age-dependent; skills learned long ago might be
easier to locate in an older invididual than in a younger one.

(This assumes that the memories of yore which suddenly seem to resurface are
in fact being correctly recollected!).

Peter "I can't recall just now - ask me in twenty years time" Brooks