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..
Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity
Authors: Julian Rossbroich, Friedemann Zenke
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate that our learning rule performs comparable to BP in deep neural networks trained on computer vision benchmarks. [...] Table 1: Test accuracy in % for networks trained with BP or dis-inhibitory feedback control. Reported values are mean stdev (n = 10). |
| Researcher Affiliation | Academia | 1 Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland 2 Faculty of Science, University of Basel, Basel, Switzerland |
| Pseudocode | No | The paper provides mathematical equations and descriptions of the model and learning rules (e.g., Eq. 9, 10, 11), but it does not include a distinct pseudocode block or algorithm figure. |
| Open Source Code | Yes | The code to reproduce our results is publicly available at https://github.com/fmi-basel/disinhibitory-control. |
| Open Datasets | Yes | To that end, we implemented a MLP with excitatory-inhibitory microcircuit units. Networks were comprised of either one or three hidden layers and used a parameterized soft rectifier activation function for all units. [...] We trained the networks on MNIST [54] and Fashion-MNIST [55]. |
| Dataset Splits | Yes | Table S3: Validation accuracy in % for networks trained with BP or dis-inhibitory feedback control. Reported values are mean +/stdev (n = 10). (See Appendix C.3 for full table). |
| Hardware Specification | Yes | All numerical simulations were implemented using Python 3.8.10 and were executed on NVIDIA Quadro RTX 5000 GPUs. |
| Software Dependencies | Yes | All numerical simulations were implemented using Python 3.8.10 and were executed on NVIDIA Quadro RTX 5000 GPUs. The software stack includes Jax [9], Flax [10], and Diffrax [11]. |
| Experiment Setup | Yes | Table S1: Hyperparameters used for training networks on the student-teacher task (Fig. 4). [...] Table S2: Hyperparameters used for training networks on computer vision benchmarks. [...] Network weights were initialized using Xavier initialization and trained for 50 epochs with a batchsize of 100. [...] For all experiments, we used the ADAM optimizer with with learning rate 0.001. |