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..
Mitigating Catastrophic Forgetting in Spiking Neural Networks through Threshold Modulation
Authors: Ilyass Hammouamri, Timothée Masquelier, Dennis George Wilson
TMLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments on different datasets show that the neurmodulated SNN can mitigate forgetting significantly with respect to a fixed threshold SNN. We also show that the evolved Neuromodulatory Network can generalize to multiple new scenarios and analyze its behavior. |
| Researcher Affiliation | Academia | Ilyass Hammouamri EMAIL Cer Co CNRS UMR 5549, Université Toulouse III, Toulouse, France Timothée Masquelier EMAIL Cer Co CNRS UMR 5549, Université Toulouse III, Toulouse, France Dennis Wilson EMAIL ISAE-Supaero, Université de Toulouse, Toulouse, France |
| Pseudocode | Yes | Algorithm 1 : Neuromodulated training step |
| Open Source Code | Yes | Code available at https://github.com/Thvnvtos/Nm-SNN |
| Open Datasets | Yes | We use two different type of datasets: a neuromorphic dataset DVS128 Gesture (Amir et al., 2017) composed of hand gestures captured with an event-based camera, and static image datasets EMNIST (Extended-MNIST) letters (Cohen et al., 2017) composed of images of handwritten uppercase and lowercase letters and MNIST (Deng, 2012). |
| Dataset Splits | Yes | Moreover, each dataset D is divided into 80% training instances and 20% testing instances. [...] For DVS128 Gesture, the evolution configuration is as follows: we use the classes from Devo with n = 3 sequential tasks, each class is learned through 20 SGD updates where we have k = 40 instances of each class and a batch size of 2, each instance consists of T = 16 frames [...] For the third test, due to having only 11 different classes, we mix Devo and Dtest [...] to obtain n = 6 sequential tasks, we note this dataset as D6. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU model, CPU type, memory). |
| Software Dependencies | No | We used Spiking Jelly (Fang et al., 2020) which is a Py Torch-based open-source deep learning framework for SNNs. This mentions the framework but not specific version numbers for Spiking Jelly or PyTorch. |
| Experiment Setup | Yes | For DVS128 Gesture, the evolution configuration is as follows: we use the classes from Devo with n = 3 sequential tasks, each class is learned through 20 SGD updates where we have k = 40 instances of each class and a batch size of 2, each instance consists of T = 16 frames [...] The evolution of the Nm N lasted approximately 600 generations until convergence. [...] EMNIST letters [...] Each class is learned through 20 SGD updates with a batch size of 4 and 80 instances. [...] We ran the evolution for approximately 1000 generations using the next 5 letters. |