Mitigating Catastrophic Forgetting in Spiking Neural Networks through Threshold Modulation
Authors: Ilyass Hammouamri, Timothée Masquelier, Dennis George Wilson
TMLR 2022 | Venue PDF | Archive PDF | Plain Text | 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. |