Understanding sensory systems relies on characterizing the stimulus-response properties of neurons at each stage of processing. This characterization can then be used to investigate the neural mechanisms underlying sensory maps and to derive computational models of sensory processing. Such knowledge will enable us to better understand the computations performed by the human brain in general and in particular will provide insights on how to develop better sensory prosthetics. We propose to develop a novel suite of quantitative methods for objectively characterizing the nonlinear responses of sensory neurons. A unique feature of our methods is that they can be used with complex, naturalistic stimuli as well as with conventional simple stimuli commonly used in sensory neurophysiology. The powerful combination of nonlinear analysis and complex stimuli potentially enables us to characterize sensory neurons even at relatively high levels of sensory processing. Much of our proposal focuses on developing the quantitative computational tools necessary for our analyses. First, we propose to develop algorithms for estimating nonlinear receptive field profiles of sensory neurons from responses to arbitrary stimuli. Second, we propose to develop tools for evaluating the quality of the resulting receptive field models. Third, we will develop analysis tools that will facilitate the biological interpretation of the derived models. Finally, we propose to develop a comprehensive software package that will include both linear and nonlinear estimation techniques as well as the evaluation and analysis tools. The package will consist of a stand alone documented function library for the most experienced users, a turn-key package with an integrated graphical user interface for general users and extensive online documentation. The tools and analyses will be validated using computational models and data collected in our laboratories and in other laboratories that have agreed to aid us in software testing and evaluation.
