Enabling hyperparameter optimization in sequential autoencoders for spiking neural data
Authors: Mohammad Reza Keshtkaran, Chethan Pandarinath
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | When applied to data from motor cortex recorded while monkeys made reaches in various directions, large-scale HP optimization allowed SAEs to better maintain performance for small dataset sizes. |
| Researcher Affiliation | Academia | Mohammad Reza Keshtkaran Coulter Dept. of Biomedical Engineering Emory University and Georgia Tech Atlanta, GA 30322 mkeshtk@emory.edu; Chethan Pandarinath Coulter Dept. of Biomedical Engineering Dept of Neurosurgery Emory University and Georgia Tech Atlanta, GA 30322 chethan@gatech.edu |
| Pseudocode | No | The paper describes the LFADS architecture and its components but does not provide any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper mentions using a 'publicly-available LFADS codepack' but does not provide a link or explicit statement about making the code for their specific contributions (Sample Validation, Coordinated Dropout, HP optimization framework) publicly available. |
| Open Datasets | Yes | The second dataset we analyzed is publicly available (indy_20160426_01 [25]). [25] Joseph E. O Doherty, Mariana M. B. Cardoso, Joseph G. Makin, and Philip N. Sabes. Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology [Data set], May 2017. Zenodo. http://doi.org/10.5281/zenodo.583331. |
| Dataset Splits | Yes | In all cases, 80% of trials were used for model training, while 20% were held-out for validation. |
| Hardware Specification | No | The paper mentions running experiments on 'a local cluster' but does not provide specific details about the hardware specifications such as GPU or CPU models. |
| Software Dependencies | No | The paper mentions using a 'publicly-available LFADS codepack' but does not specify any software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Table 1: List of HPs searched with PBT: L2 Gen scale (5, 5e4) log-uniform, L2 Con scale (5, 5e4) log-uniform, KL IC scale (0.05, 5) log-uniform, KL CO scale (0.05, 5) log-uniform, Dropout (0, 0.7) uniform, Keep ratio (0.3, 0.99) 0.5, Learning rate (10 5, 0.02) 0.01. |