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adaptiveOptimalTraining

This package includes the matlab code for the paper:

  • Ji Hyun Bak, Jung Yoon Choi, Athena Akrami, Ilana Witten, Jonathan Pillow. (2016) Adaptive optimal training of animal behavior. Advances in Neural Information Processing Systems 29. [link]

Sample scripts:

getSimDat.m: generates a simulated behavior dataset (run this first!) - surrogate for a real animal behavior dataset.

AOT_script_estWgt.m: script for analyzing past observations,

  • first with the random-walk prior only (hyperparameter sigma)
    • corresponds to Fig 2 in paper
  • then with added learning component as drift (hyperparameter alpha)
    • corresponds to Fig 3 in paper

AOT_script_training.m: script for simulated active/passive training - corresponds to Fig 4 and S2 in paper

Core functions:

  • funs_MNLogistic.m: (this is a script) contains basic operations for (multinomial) logistic model usually called at the beginning of each core function

  • getMAP_RWprior.m: does the MAP estimate for the weights with the random walk prior

    • getLP_MNLogistic_RWprior (core external subfunction)
    • negLogPost_MNLRW (wrapper for getLP)

  • getSimRat_active.m: runs a simulated active training experiment

    • calls getPolGrad_discrimTask.m

  • getPolGrad_discrimTask.m: calculates the policy gradient and the higher gradients, taylored for the specific task / model structure