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Codepack accompanying "Internal models for interpreting neural population activity during sensorimotor control," by Matthew D. Golub, Byron M. Yu, and Steven M. Chase, eLife 2015.

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This is the codepack to accompany "Internal models for interpreting neural population activity during sensorimotor control," by Matthew D. Golub, Byron M. Yu, and Steven M. Chase, eLife 2015. The original article can be found at: https://elifesciences.org/articles/10015/

Codepack version: 2.0 Written by: Matt Golub Additional testing by: Jay Hennig

Feedback, comments, suggestions, bug reports, etc, are welcomed and encouraged. Please direct correspondence to [email protected].

MATLAB VERSIONS:

This codepack was developed and tested using Matlab R2015a for Mac. We have also had success with Matlab R2011, R2016a, R2017b, and R2018a for Mac, and Matlab R2018a for Linux . This codepack may not be compatible with earlier Matlab versions.

DESCRIPTION:

This codepack includes:

  1. Three example scripts demonstrating various usages of the internal model estimation (IME) framework.

    example1.m: A minimal working example of IME fitting, prediction, and evaluation. This example does not use proper cross validation procedures, which are critical when drawing scientific and engineering interpretations from results.

              Loads some example BMI data, fits an IME model, extracts
              the subject's internal estimates of cursor position and 
              velocity, and evaluate errors in the actual cursor 
              trajectory and in the subject's internal estimates. This
              script will also generate two figures: one quantifying 
              those errors; and a second showing a cursor trajectory and
              the subject's internal state estimates from an example 
              trial. Each of these procedures can be ported for usage on 
              new datasets as desired by leveraging 2-6 below.
    

    example2.m: Same as example1, but using proper cross validation techniques.

    example3.m: Demonstrates model selection techniques for choosing the value of TAU, the visual feedback delay used in IME models.

  2. The EM algorithm for fitting an IME model. velime_fit.m

  3. Code for extracting the subject's internal estimates of cursor position and velocity according to the internal model. velime_predict.m

  4. Code for evaluating and plotting the angular errors in the actual cursor trajectories and according to the subject's internal model. Errors are presented in the style of Figure 3C of the paper. velime_evaluate.m plot_angular_aiming_errors.m

  5. Code for visualizing single trial cursor trajectories, overlaid at each timestep with the subject's internally predicted evolution of the cursor trajectory given the most recently available visual feedback of cursor position and the subsequently issued neural commands. These visualizations are in the style of those in Figure 4A,B of the paper. plot_trials_with_whiskers.m

  6. Data from a representative closed-loop BMI experiment from the paper (dataset A010509), upon which 2-5 are applied. When applying IME to new datasets, data should be formatted according the conventions of these example data (field names, temporal relationships, and trial structure). example_data.mat

AKNOWLEDGEMENTS:

This codepack leverages code written by Paolo de Leva for fast array expansion, multidimensional array multiplication and multidimensional array transposing (baxfun, multiprod, and multitransp, respectively; Copyright (c) 2009, Paolo de Leva, all rights reserved.). The license for this code allows for redistribution provided that the following list of conditions are conveyed:

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

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Codepack accompanying "Internal models for interpreting neural population activity during sensorimotor control," by Matthew D. Golub, Byron M. Yu, and Steven M. Chase, eLife 2015.

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