Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
GAIT-prop: A biologically plausible learning rule derived from backpropagation of error
Authors: Nasir Ahmad, Marcel A. J. van Gerven, Luca Ambrogioni
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 15,059 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,893 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In a series of simple computer vision experiments, we show near-identical performance between backpropagation and GAIT-prop with a soft orthogonality-inducing regularizer. |
| Researcher Affiliation | Academia | Nasir Ahmad Marcel van Gerven Luca Ambrogioni Department of Artificial Intelligence Donders Institute for Brain, Cognition and Behaviour Radboud University, Nijmegen, the Netherlands EMAIL |
| Pseudocode | Yes | Algorithm 1 GAIT-prop (per training sample update) |
| Open Source Code | Yes | Code used to produce the results shown in this paper is available at https://github.com/nasiryahm/GAIT-prop. |
| Open Datasets | Yes | We make use of three image classification datasets: MNIST, Fashion-MNIST, and KMNIST. |
| Dataset Splits | No | The paper mentions training and testing, and a grid search for parameters, but does not explicitly detail the training/validation/test splits, such as percentages or sample counts for each split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of 'Adam optimiser' but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | No | In order to identify acceptable parameters for each of our learning methods, we ran a grid search for the learning rate η and the orthogonal regularizer strength λ. The highest-performing networks were tested for stability and stable high performing parameters were used. Details of specific parameters used and the grid search outcomes are provided in the Supplementary Material. |