A Biologically Plausible Neural Network for Slow Feature Analysis
Authors: David Lipshutz, Charles Windolf, Siavash Golkar, Dmitri Chklovskii
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate our approach, we test our algorithm on datasets of naturalistic stimuli and reproduce results originally performed in the offline setting. |
| Researcher Affiliation | Academia | 1 Center for Computational Neuroscience, Flatiron Institute 2 Department of Statistics, Columbia University 3 Neuroscience Institute, NYU Medical Center |
| Pseudocode | Yes | Algorithm 1: Bio-SFA |
| Open Source Code | Yes | The evaluation code is available at https://github.com/flatironinstitute/bio-sfa. |
| Open Datasets | Yes | We test Bio-SFA on a sequence of natural images. First, a 256-dimensional sequence {zt} was generated by moving a 16 16 patch over 13 natural images from [12] via translations, zooms, and rotations. Following Schönfeld and Wiskott [25], we test a hierarchical 3-layer organization of Bio-SFA modules on the inputs from the Rat Lab framework [24]. |
| Dataset Splits | No | The paper does not explicitly provide specific train/validation/test dataset splits, percentages, or cross-validation details needed to reproduce the data partitioning. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | No | The paper mentions parameters for Algorithm 1 (γ, ε) but does not provide their specific values. It describes the general training strategy ('trained greedily layer-by-layer with weight sharing') and architectural choices but lacks concrete hyperparameters (e.g., learning rate, batch size, number of epochs) or system-level training settings. |