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 are to study signal processing carried out by neuron in several populations of neurons which do not yield to linear systems analysis. Quite often cortical neurons require multiple filters to describe their coding, with diverse nonlinear operations on the filtered inputs. The Teragrid resource will be used to find the two most relevant filters for neurons (1) in the primary auditory cortex with the goal of characterizing the diversity of signal processing that takes place across the six cortical layers; (2) in the auditory forebrain of songbirds in order to determine their profile in both frequency and time; (3) in the lateral geniculate nucleus of primates where, which is a system where both inputs and outputs to the circuit can be analyzed, so taht we can study the transformation in filtering carried out by single neurons; (4) the rodent visual cortex that is overall relativel less described compraed to other species. The relevant neural filters are found by maximizing information Shannon muutal information between the sequence of neural responses and input stimuli filtered by them (Sharpee et al Neural Computation 2004). We employ a C++ code with open MP parallelization, and follow a simulated annealing scheme combined with a search along the gradient of information. The task is well suited for parallel computation, because calculations for each neuron are independent of each other. The obtained relevant neural fitlers are then visualized and analyzed using local resources to provide insights into the typical feature selectivity and organization of the populations of neurons listed above