Our understanding the physiology of human brain relies on the detailed description of the mapping between sensory stimuli and neural responses. These mappings are complex as they involve non-linear transformations, dynamics, learning and adaptation, feedback and priors due to natural or learned constraints. Inspired by recent advances in the field of machine learning, we will investigate the use of a series of algorithms that will allow us to characterize such complex mappings. The algorithms represent modern improvements to the classical engineering systems analysis approach that could only be applied to the most peripheral stages of sensory processing. These new algorithms use the power of modern computers to efficiently find the simplest set of functions that describe dynamical and non-linear mappings. Our first two aims focus on development of parametric and non-parametric algorithms to characterize general stimulus response mappings. The parametric models include explicit formulations of adaptive gain control and feedback. The non-parametric models are estimated from the data using several algorithms, including maximally informative dimensions, neural network analyses, Lasso regression and kernel regression.
Our third aim i s to develop methods to validate various models. We also propose to make these tools available to the neuroscience community at large by incorporating them into STRFPAK, a software package (released during the previous award period and undergoing continuous improvement) for estimating receptive fields of sensory neurons. Finally, we propose to develop a database that will serve as a repository for neuro-physiological data and analysis tools developed in the community. The database will encourage the validation and distributions of data analysis methods. The database will also provide experimental data to theorist and modelers of brain function. The stimulus-response mapping algorithms developed under this proposal will provide neurobiologists with quantitative tools previously only available to specialists. They therefore will have a direct benefit on basic research. A thorough understanding of the complex stimulus-response mapping of sensory systems will also have significant benefits for several areas of medicine: evaluation and diagnosis of disease states such as macular degeneration, and improvements in neural prosthetics such as hearing aids. The computational mechanisms governing how the brain represents the sensory world are in many respects similar to those governing how the brain translates motor intention to motor action. Therefore, application of these algorithms is also likely to lead to eventual improvements in neural prosthetics for motor control and action.
