Dis-inhibitory neuronal circuits can control the sign of synaptic plasticity
Authors: Julian Rossbroich, Friedemann Zenke
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | 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. |