Adaptive optimal training of animal behavior
Authors: Ji Hyun Bak, Jung Yoon Choi, Athena Akrami, Ilana Witten, Jonathan W. Pillow
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We develop and test these methods using data collected from rats during training on a two-interval sensory discrimination task. We show that we can accurately infer the parameters of a policy-gradient-based learning algorithm that describes how the animal s internal model of the task evolves over the course of training. Simulations show that our method can in theory provide a substantial speedup over standard training methods. |
| Researcher Affiliation | Academia | Ji Hyun Bak1,4 Jung Yoon Choi2,3 Athena Akrami3,5 Ilana Witten2,3 Jonathan W. Pillow2,3 1Department of Physics, 2Department of Psychology, Princeton University 3Princeton Neuroscience Institute, Princeton University 4School of Computational Sciences, Korea Institute for Advanced Study 5Howard Hughes Medical Institute jhbak@kias.re.kr, {jungchoi,aakrami,iwitten,pillow}@princeton.edu |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access information for open-source code. |
| Open Datasets | No | The paper refers to an "actual experimental dataset from rats" and cites a conference abstract ([3]) for it, but does not provide specific access information like a URL, DOI, or repository for public access to the dataset. The dataset is not a well-known public benchmark dataset. |
| Dataset Splits | No | The paper mentions applying its method to a simulated dataset and an experimental dataset from rats. It discusses hyperparameter optimization using 'evidence maximization' and BIC for model selection, but it does not specify explicit train/validation/test splits of data for model evaluation in the conventional sense (e.g., 80/10/10 split). |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions using "the Matlab function fminunc" in Section 3.3 but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The two hyperparameters {σsim, αsim} were chosen such that the resulting time series data is qualitatively similar to the rat data. The simulated learning model can be recovered by maximizing the evidence (11), now with the learning hyperparameter α as well as the variability σ. The solution accurately reflects the true αsim, shown where σ is fixed at the true σsim (Figures 3A-3B). We tested the Align Max training protocol using a simulated learner with fixed hyperparameters αsim = 0.005 and σsim = 0, using wgoal = (b, a1, a2, h)goal = (0, 10, 10, 0) in the current paradigm. |