Designing neurophysiology experiments to optimally
constrain receptive field models along parametric
submanifolds
NIPS, 2008.
Sequential optimal design methods hold great promise for improving the
efficiency of neurophysiology experiments. However, previous methods
for optimal experimental design have incorporated only weak prior
information about the underlying neural system (e.g., the sparseness
or smoothness of the receptive field). Here we describe how to use
stronger prior information, in the form of parametric models of the
receptive field, in order to construct optimal stimuli and further
improve the efficiency of our experiments. For example, if we believe
that the receptive field is well-approximated by a Gabor function,
then our method constructs stimuli that optimally constrain the Gabor
parameters (orientation, spatial frequency, etc.) using as few
experimental trials as possible. More generally, we may believe a
priori that the receptive field lies near a known sub-manifold of the
full parameter space; in this case, our method chooses stimuli in
order to reduce the uncertainty along the tangent space of this
sub-manifold as rapidly as possible. Applications to simulated and
real data indicate that these methods may in many cases improve the
experimental efficiency by an order of magnitude.
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