MT-SNN: Spiking Neural Network that Enables Single-Tasking of Multiple Tasks
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Abstract (arXiv)
In this paper we explore capabilities of spiking neural networks in solving multi-task classification problems using the approach of single-tasking of multiple tasks. We designed and implemented a multi-task spiking neural network (MT-SNN) that can learn two or more classification tasks while performing one task at a time. The task to perform is selected by modulating the firing threshold of leaky integrate and fire neurons used in this work. The network is implemented using Intel's Lava platform for the Loihi2 neuromorphic chip. Tests are performed on dynamic multitask classification for NMNIST data. The results show that MT-SNN effectively learns multiple tasks by modifying its dynamics, namely, the spiking neurons' firing threshold.
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References · 9
- 2014Unsupervised Domain Adaptation by Backpropagationcited by 1,906
- 2020Multi-Task Learning with Deep Neural Networks: A Surveycited by 374
- 2015Converting Static Image Datasets to Spiking Neuromorphic Datasets Using Saccadescited by 308
- 2018Deep Learning in Spiking Neural Networkscited by 258
- 2018SLAYER: Spike Layer Error Reassignment in Timecited by 257
- 2018Efficient parametrization of multi-domain deep neural networkscited by 168
- 2019Attentive Single-Tasking of Multiple Taskscited by 148
- 2017Adversarial Multi-task Learning for Text Classificationcited by 91
- 2017Universal representations:The missing link between faces, text, planktons, and cat breedscited by 45
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