Automating the design of informative sequences of sensory stimuli

Authors:
Jeremy Lewi;David M. Schneider;Sarah M. Woolley;Liam Paninski
Affiliations:
Georgia Institute of Technology, Atlanta, USA 30332;Columbia University, New York, USA 10027-6902;Columbia University, New York, USA 10027-6902;Columbia University, New York, USA 10027-6902
Venue:
Journal of Computational Neuroscience
Year:
2011

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Abstract

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.