Experimental Data

Neural Data (optional)

  1. Save the recorded neural data for the training and testing conditions in ‘./nusim_neural_data/neural_activity.pkl’ as a Python dict object

dict{

   <'train'> : <dict_train>,

   <'test'> : <dict_test>

}

  1. <dict_train> and <dict_test> are Python dictionary objects that contain the neural data in the following format:

    <key: int> is the integer index of the corresponding condition as in the kinematics file.

    <value: numpy.ndarray> is the numpy array that contains recorded firing rates with the following shape: [timepoints, num_neurons]. num_neurons are the total number of recorded neurons.

Note

If this step is omitted, various post-training quantitative analyses which require recorded neural data such as CCA, will not run. nuSim training will also not proceed (nusim_data_path can also be specified in the ./configs/configs.txt file).

Stimulus Data (optional)

Provide any experimental stimulus data in ./stimulus_data/stimulus_data.pkl as a Python dict object:

dict{

   <'train'> : <dict_train>,

   <'test'> : <dict_test>

}

  1. <dict_train> and <dict_test> are Python dictionary objects that contain the experimental stimulus data in the following format:

    <key: int> is the integer index of the corresponding condition as in the kinematics file.

    <value: numpy.ndarray> is the numpy array that contains recorded stimulus data with the following shape: [timepoints, num_features]. num_features are the corresponding features in that stimulus.

Initial Pose Data (optional)

Save the initial pose (containing the qpos and qvel) as numpy arrays in ./inital_pose/ as qpos.npy and qvel.npy with shape [nq, ]. nq is the number of joints in the xml model.

This step is optional. If omitted, the default initial pose for xml model will be used for CMA-ES and IK.

(initial_pose_path can also be specified in the ./configs/configs.txt file)