Spiking neural networks, the third generation of neural networks, offer a biologically plausible alternative to traditional deep learning models. They promise low-power applications for artificial intelligence, enabling a lower footprint for such algorithms that were becoming more energy-consuming with the venue of large language models. The leaky integrate-and-fire (LIF) is the most common neuron model used for spiking neural networks due to its simplicity and low power consumption while remaining biologically plausible. This paper compares the performance of optimized LIF variants from recurrent to synaptic conductance-based models that are then applied across four well-known image classification benchmarks and two real-world cybersecurity and financial fraud datasets. The experiments assess the performance of these neurons within a spiking window size of 10 and 50 time steps, exploring the trade-off between performance and energetic consumption of each LIF variant. Our results show that the LIF neuron offers the best performance and energy consumption tradeoff among the tested LIF variants. However, in some cases, other variants can outperform the standard LIF neuron model with the cost of a higher footprint.