This study focuses on the integration of the Izhikevich neuron model into the NeuCube framework to improve classification accuracy in longitudinal data. NeuCube is a reservoir spiking neural network architecture and offers advanced features such as spatial neuron connectivity and biologically plausible learning, making it suitable for complex machine learning tasks. For extracting meaningful features from the spiking activity of NeuCube, we evaluated various sampling methods on the classification performance of control and UHR samples from the LYRIKS RNA sequencing dataset. Our findings demonstrate that feature extraction using temporal binning, particularly with 10 bins, yields the highest accuracy across neuron types by effectively capturing fine-grained temporal dynamics. Intrinsic bursting neurons showed superior accuracy across most sampling methods, underscoring their versatility in information transmission.