The outbreak of a global pandemic called coronavirus has created unprecedented circumstances resulting into a large number of deaths and risk of community spreading throughout the world . Desperate times have called for desperate measures to detect the disease at an early stage via various medically proven methods like chest computed tomography (CT) scan, chest X-Ray, etc., in order to prevent the virus from spreading across the community . Developing deep learning models for analysing these kinds of radiological images is a well-known methodology in the domain of computer based medical image analysis . However, doing the same by mimicking the biological models and leveraging the newly developed neuromorphic computing chips might be more economical . These chips have been shown to be more powerful and are more efficient than conventional central and graphics processing units . Additionally, these chips facilitate the implementation of spiking neural networks (SNNs) in real-world scenarios . To this end, in this work, we have tried to simulate the SNNs using various deep learning libraries . We have applied them for the classification of chest CT scan images into COVID and non-COVID classes . Our approach has achieved very high F1 score of 0.99 for the potential-based model and outperforms many state-of-the-art models . The working code associated with our present work can be found here.