Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
A Biologically Plausible Neural Network for Slow Feature Analysis
Authors: David Lipshutz, Charles Windolf, Siavash Golkar, Dmitri Chklovskii
NeurIPS 2020 | Venue PDF | 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. |