Quadrotors have continuously leveraged the use of artificial intelligence for navigation and decision-making. Moreover, neuromorphic computing, specifically Spiking Neural Networks (SNNs), is considered as an energy-efficient solution during inference. The current study will analyse the effects of implementing SNNs for mimicking energy optimal guidance and control. To achieve this, population encoding is used and an equivalent of 7-8 spiking neurons per conventional neuron is found to preserve most of the information. The equivalent controller prefers fast adaptation which requires small spiking threshold values and minimal reliance on past information. To improve the controller performance, dataset selection is of utmost importance with a careful trade-off between excessive race track customisation and generalisability being required. The results show that learning is feasible and SNN performance approaches conventional state-of-the-art models trained with multi-layer perceptrons. The current analysis represent an important step towards the rapid guidance and control of ultra-small energy efficient quadrotors.