Neural network module

DEERNet is a collection of functions that make and use deep neural networks for processing DEER data. The approach is described in detail in https://dx.doi.org/10.1126/sciadv.aat5218

Using DEERNet

Simply feed your DEER data to deernet.m or download DeerAnalysis2018, which is easy to use and has DEERNet integrated.

Training your own networks

1. Use deer_lib_gen.m to build a training database.
2. Use train_one_net.m repeatedly to create a netset.
3. Use netset_curate.m to eliminate low-performance networks.
4. Use the resulting netset with deernet.m or deernet_bckg.m

Functions

deer_lib_gen.m

Generates a library of simulated DEER data for use in neural network training and validation.

deer_resample.m

Resamples a DEER trace to fit the number of digitsation points expected by the neural network.

deernet.m

Uses a curated ensemble of neural networks to extract the distance distribution from DEER data.

deernet_bckg.m

Uses a curated ensemble of neural networks to extract the background signal from DEER data.

deernet_wrapper.m

Compilable DEERNet wrapper for use outside Matlab and Spinach.

descramble.m

Weight matrix descrambling using Tikhonov smoothness criterion.

dist_range.m

Distance range estimation for a given time grid.

elexsys2deernet.m

Prepares standard Bruker Elexsys datasets for input into the deernet.m function.

left_diag.m

Weight matrix descrambling using maximum diagonality criterion.

netset_curate.m

Evaluates an ensemble of neural networks and decides which ones are best.

process_using.m

Runs DEER data processing using a specified neural network file.

train_one_net.m

Trains a single neural network using supplied parameters.

Version 2.5, authors: Ilya Kuprov, Steve Worswick, Jake Amey, Jake Keeley, Tajwar Choudhury