Efficient inference for time-varying behavior during learning
Authors: Nicholas A. Roy, Ji Hyun Bak, Athena Akrami, Carlos Brody, Jonathan W. Pillow
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To illustrate performance, we apply our method to psychophysical data from both rats and human subjects learning a delayed sensory discrimination task. |
| Researcher Affiliation | Academia | Nicholas A. Roy1 Ji Hyun Bak2 Athena Akrami1,3, Carlos D. Brody1,3,4 Jonathan W. Pillow1,5 1Princeton Neuroscience Institute, Princeton University 2Korea Institute for Advanced Study 3Howard Hughes Medical Institute 4Dept. of Molecular Biology, 5Dept. of Psychology, Princeton University current address at Sainsbury Wellcome Centre, UCL |
| Pseudocode | Yes | Algorithm 1 Optimizing hyperparameters with the decoupled Laplace approximation |
| Open Source Code | Yes | An implementation of all methods are available as the Python package Psy Track [6]. |
| Open Datasets | Yes | We apply our method to psychophysical data from both rats and human subjects performing a 2AFC delayed response task, as reported in [17]. Reference [17]: Athena Akrami, Charles D Kopec, Mathew E Diamond, and Carlos D Brody. Posterior parietal cortex represents sensory history and mediates its effects on behaviour. Nature, 554(7692):368, 2018. |
| Dataset Splits | Yes | Predicted performance and bias are calculated using cross-validated weights (calculations and cross-validation procedure detailed in Secs.S3 & S4). |
| Hardware Specification | No | The paper mentions computation time 'in minutes on a desktop computer' and 'on a laptop', but does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper mentions 'Sci Py' and 'Python package Psy Track' but does not provide specific version numbers for these software components or any other software dependencies, which is required for reproducibility. |
| Experiment Setup | Yes | We optimize hyperparameters governing how rapidly each weight evolves over time using the decoupled Laplace approximation, an efficient method for maximizing marginal likelihood in non-conjugate models. In practice, we also parametrize θ by fixing σk,t=0 = 16, an arbitrary large value that allows the likelihood to determine w0 rather than forcing the weights to initialize near some predetermined value. |