  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.
