Automating the design of informative sequences of sensory
stimuli
In press, J. Comput. Neuro.
Adaptive stimulus design methods can potentially improve the
efficiency of sensory neurophysiology experiments significantly;
however, designing optimal stimulus sequences in real time remains a
serious technical challenge. Here we describe two approximate methods
for generating informative stimulus sequences: the first approach
provides a fast method for scoring the informativeness of a batch of
specific potential stimulus sequences, while the second method
attempts to compute an optimal stimulus distribution from which the
experimenter may easily sample. We apply these methods to
single-neuron spike train data recorded from the auditory midbrain of
zebra finches, and demonstrate that the resulting stimulus sequences
do in fact provide more information about neuronal tuning in a shorter
amount of time than do more standard experimental designs.
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