  Redundant information transfer in a neural network can increase the
complexity of the deep learning model, thus increasing its power consumption.
We introduce in this paper a novel spiking neuron, termed Variable Spiking
Neuron (VSN), which can reduce the redundant firing using lessons from
biological neuron inspired Leaky Integrate and Fire Spiking Neurons (LIF-SN).
The proposed VSN blends LIF-SN and artificial neurons. It garners the advantage
of intermittent firing from the LIF-SN and utilizes the advantage of continuous
activation from the artificial neuron. This property of the proposed VSN makes
it suitable for regression tasks, which is a weak point for the vanilla spiking
neurons, all while keeping the energy budget low. The proposed VSN is tested
against both classification and regression tasks. The results produced advocate
favorably towards the efficacy of the proposed spiking neuron, particularly for
regression tasks.
