Despite the heterogeneous organization with regard to signal correlation, neurons tended to have similar noise correlation during on-going and tone-driven activities, suggesting that noise correlations reflect structure in the local network (see below). 2. Relate your findings to the issues you raised in the introduction. Note similarities, differences, common or different trends.
The simplest connectivity model to consider would be that of a tonotopic input combined with locally random (noisy) connectivity. This model could partially explain our results, including the heterogeneous micro-architecture and the decrease of signal and noise correlations with distance. Our data, however, put additional constraints on this connectivity model.
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Such a model would result in the formation of small subnetworks of highly correlated neurons , partially overlapping in space. A number of our findings support such a subnetworks model. Notably, a subnetworks model would account for the details of the dependence of signal correlation and noise correlation on distance. Specifically, at short distances we observed both very large and very small correlations, while at longer distances we observed only smaller correlations.
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Professors are nice to undergrads, but it’s a fundamentally different relationship. They treat grad students as peers not students. I think you’ll find the environment far more conducive to the sort of work you want to do.