  Exoplanets are celestial bodies orbiting stars beyond our Solar System.
Although historically they posed detection challenges, Kepler's data has
revolutionized our understanding. By analyzing flux values from the Kepler
Mission, we investigate the intricate patterns in starlight that may indicate
the presence of exoplanets. This study investigates a novel approach for
exoplanet classification using Spiking Neural Networks (SNNs) applied to data
obtained from the NASA Kepler mission. SNNs offer a unique advantage by
mimicking the spiking behavior of neurons in the brain, allowing for more
nuanced and biologically inspired processing of temporal data. Experimental
results demonstrate the efficacy of the proposed SNN architecture, excelling in
various performance metrics such as accuracy, F1 score, precision, and recall.
