Spiking neural networks (SNNs) aim to utilize mechanisms from biological neurons to bridge the computational and efficiency gaps between the human brain and machine learning systems. The widely used Leaky-Integrate-and-Fire (LIF) neuron model accumulates input spikes into an exponentially decaying membrane potential and generates a spike when this potential exceeds a set threshold. A LIF neuron is characterized by learnable input weights and a manually selected membrane time constant 𝜏, which determines the decay rate of a neuron's membrane potential. Previous work introduced the Parametric LIF (PLIF) neuron model with a learnable 𝜏. However, the published experiments only featured a single 𝜏 per spiking layer. This leaves space for exploration of the effect of having a learnable 𝜏 for each neuron. The importance of 𝜏 is given by the trade-off it inherently introduces, prioritizing the neuron's capability to convey information about spatial or temporal features. This work examines the effect of introducing a learnable 𝜏 per neuron with a new initialization method and a new regularization term which incentivizes low variance in each PLIF layer. The experiments are done using the DVS128 Gesture dataset and compared to a baseline model from the original paper introducing the PLIF neuron model. Results are inconclusive but suggest that introducing 𝜏 per neuron does not have a significant effect on the accuracy of a spiking neural network. Moreover, the evolution of 𝜏 during training exhibits interesting behavior and leads to two new hypotheses.