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.
Biological credit assignment through dynamic inversion of feedforward networks
Authors: Bill Podlaski, Christian K. Machens
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,861 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,957 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 | We tested dynamic inversion (DI) and non-dynamic inversion (NDI) against backpropagation (BP), feedback alignment (FA), and pseudobackprop (PBP) on four modest supervised and unsupervised learning tasks linear regression, nonlinear regression, MNIST classification, and MNIST autoencoding. |
| Researcher Affiliation | Academia | William F. Podlaski Champalimaud Research Champalimaud Centre for the Unknown 1400-038 Lisbon, Portugal Christian K. Machens Champalimaud Research Champalimaud Centre for the Unknown 1400-038 Lisbon, Portugal Correspondence: EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks (e.g., a section labeled 'Algorithm' or 'Pseudocode'). |
| Open Source Code | No | The paper does not provide any concrete access information (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | Yes | We next tested dynamic inversion on the MNIST handwritten digit dataset, where we use the standard training and test datasets (Le Cun et al., 1998)... |
| Dataset Splits | No | The paper mentions using 'standard training and test datasets' for MNIST, but does not explicitly provide details for a separate validation split or specific percentages/counts for all three splits (train/validation/test). |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | Learning rate =10 2 for all algorithms. and DI was simulated numerically using 1000 Euler steps with dt = 0.5. and mini-batch training (100 examples per batch). and learning rate = 10 3 for all algorithms. and learning rate = 10 6 for all algorithms. |