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