High resolution neural connectivity from incomplete tracing data using nonnegative spline regression
Authors: Kameron D. Harris, Stefan Mihalas, Eric Shea-Brown
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the consistency of our estimator using synthetic data and then apply it to newly available Allen Mouse Brain Connectivity Atlas data for the visual system. Our algorithm is significantly more predictive than current state of the art approaches which assume regions to be homogeneous. |
| Researcher Affiliation | Collaboration | Kameron Decker Harris Applied Mathematics, U. of Washington kamdh@uw.edu Stefan Mihalas Allen Institute for Brain Science Applied Mathematics, U. of Washington stefanm@alleninstitute.org Eric Shea-Brown Applied Mathematics, U. of Washington Allen Institute for Brain Science etsb@uw.edu |
| Pseudocode | No | The paper describes mathematical formulations and optimization methods but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | All of our supplemental material and data processing and optimization code is available for download from: https://github.com/kharris/high-res-connectivity-nips-2016. |
| Open Datasets | Yes | We next apply our method to the latest data from the Allen Institute Mouse Brain Connectivity Atlas, obtained with the API at http://connectivity.brain-map.org. |
| Dataset Splits | Yes | In order to evaluate the performance of the estimator, we employ nested cross-validation with 5 inner and outer folds. The full rank estimator (P1) was fit for λ = 103, 104, . . . , 1012 on the training data. Using the validation data, we then selected the λopt that minimized the mean square error relative to the average squared norm of the prediction WX and truth Y, evaluating errors outside the injection sites |
| Hardware Specification | No | The paper mentions that work was facilitated 'though the use of advanced computational, storage, and networking infrastructure provided by the Hyak supercomputer system at the University of Washington', but it does not provide specific details on CPU models, GPU models, or memory. |
| Software Dependencies | No | The paper states that optimizations were 'implemented in C++' and 'in Matlab', and references specific methods like 'L-BFGS-B' and 'Nesterov acceleration', but it does not provide version numbers for any software components. |
| Experiment Setup | Yes | The full rank estimator (P1) was fit for λ = 103, 104, . . . , 1012 on the training data. In our dataset, λopt = 105 was selected for all outer folds. |