Backpropagation as litmus test for mappings?
mitchm at casbah.acns.nwu.edu
Sat Feb 13 15:05:58 EST 1993
A grad student in my lab was looking at various algorithms to model
optimal synergies of muscle activation, even though it's questionable whether
the body is really optimal or just settles for "close enough." He and I both
lean towards the latter, so he is trying to show that the nervous system is
not using any consistent optimization scheme by making comparisons between
real data and model predictions.
Since this requires a "hunt-and-peck" approach, I suggested that he
try using the real (i.e., experimental data) to try to train a backprop
network to map force measurements to EMG (or vice-versa). Since he just wants
to show whether or not a consistent relation exists, and a backprop learning
algorithm should converge to the relation if one exists, the (in)ability of a
backprop network to create a mapping should be a litmus test, right?
If not, could you tell me why? Thanks.
Mitchell Maltenfort Northwestern Unversity Chicago, Illinois
mgm at nwu.edu |:* Studying simulations or simulating studies *:|
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