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 objectives of this project are to study signal processing carried out by neurons in several populations of neurons for which linear systems analysis fails. Quite often neuron require multiple fitlers to describe their coding, and perform diverse nonlinear operations with respect to the filtered inputs. The Teragrid resources will be used to find two most relevant filters for neurons (1) in the primary auditory cortex with the goal of understanding the change in filtering that takes place across the six cortical layers; (2) in the auditory forebrain of song birds in order to determine the number of relevant filters and their characteristic shape; (3) in the lateral geniculate nucleus of macaques where both inputs and outputs to a well-defined neuronal circuit are accessible, so that we can study the transformation in filtering carried out by single neurons; (4) the rodent lateral geniculate nucleus which is overall relatively less described compared to other species. The relevant neural filters are found by maximizing Shannon information between the sequence of responses and input stimuli filtered by them. 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 filters 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.
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