STATISTICAL APPROACHES TO UNDERSTANDING NEURAL FEATURE SELECTIVITY

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This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. The goal of this project is to use advanced statistical methods for characterizing responses of neurons using stimuli that closely approximate those typically found in the natural sensory environment. Primarily we analyze filtering properties of visual and auditory neurons. To characterize neural responses we use a model where neural responses are affected by one or two relevant stimulus dimensions. Finding these stimulus dimensions is the most computationally intensive part of the analysis. We use a combination of simulated anneling and gradient ascent search to find the most appropriate selection of such relevant dimensions for each neuron. These results help further our understanding of how object recognition, eithr visual or auditory, is carried out in the nervous system