论文标题
MT-SNN：尖峰神经网络，可以实现多个任务的单任务
MT-SNN: Spiking Neural Network that Enables Single-Tasking of Multiple Tasks
论文作者
论文摘要
在本文中，我们探讨了使用多个任务的单任务方法来解决多任务分类问题的尖峰神经网络的能力。我们设计并实施了一个多任务尖峰神经网络（MT-SNN），该网络可以在一次执行一项任务时学习两个或多个分类任务。通过调节此工作中使用的泄漏的集成和消防神经元的发射阈值来选择执行的任务。该网络是使用Intel的Laihi2神经形态芯片的熔岩平台实现的。对NMNIST数据的动态多任务分类进行测试。结果表明，MT-SNN通过修改其动力学有效地学习了多个任务，即尖峰神经元的发射阈值。
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.
