Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE
Authors: Ding Zhou, Xue-Xin Wei
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We validate pi-VAE using synthetic data, and apply it to analyze neurophysiological datasets from rat hippocampus and macaque motor cortex. We demonstrate that pi-VAE not only fits the data better, but also provides unexpected novel insights into the structure of the neural codes. |
| Researcher Affiliation | Academia | Ding Zhou Department of Statistics Columbia University dz2336@columbia.edu Xue-Xin Wei Department of Neuroscience UT Austin weixx@utexas.edu |
| Pseudocode | No | The paper describes the inference procedure in text but does not provide pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for their methodology. |
| Open Datasets | Yes | We have applied pi-VAE to analyze two electrophysiology datasets, each has more than 100 simultaneously recorded neurons when the animals were performing behavioral tasks. [...] a public rat s hippocampus dataset (27; 26)1. http://crcns.org/data-sets/hc/hc-11 |
| Dataset Splits | Yes | We randomly split the dataset into 24 batches, where each batch contains at least one trial for each direction. We randomly split them into training, validation and test data (20, 2, 2 batches). [...] We randomly split them into training, validation and test data (68, 8, 8 laps). |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments, such as specific GPU/CPU models or memory details. |
| Software Dependencies | No | The paper mentions using the Adam optimizer and setting values to recommendations, but does not provide specific version numbers for any software dependencies or libraries (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | We binned the ensemble spike activities into 50ms bins. [...] We used the spike activities as observation x, and the reaching direction as the discrete labels u. [...] We fit 4-dimensional latent models to the data based on pi-VAE and VAE. [...] We binned the ensemble spike activities into 25ms bins. [...] We fit 2-dimensional latent models to the data for both pi-VAE and VAE. [...] Adam optimizer (36) with learning rate equal to 5 10 4, and other values were set to the recommendation values for all the experiments in this paper. |