This paper presents a novel spiking neural network (SNN) framework that integrates unsupervised and supervised learning to improve classification accuracy while reducing model complexity. Spatio-temporal data are modelled using a three-dimensional SNN reservoir trained via spike-timing-dependent plasticity (STDP). A dynamic clustering method is applied during STDP to analyse evolving spike patterns and prune less informative connections, resulting in a sparse, functionally rich reservoir. We propose a new supervised learning model, Neuron-Stability Weighted Dynamic Evolving Spiking Neural Network (NSW-deSNN), which extends the traditional deSNN by incorporating a stability-based weighting term. This term, derived from the entropy of neuron-cluster memberships, prioritizes contributions from stable, informative neurons when forming output connections. Tested on fMRI data related to a cognitive task (reading sentences with different polarities), the proposed approach demonstrates improved accuracy and efficiency over conventional fully connected SNN models, highlighting its potential for learning meaningful spatio-temporal patterns from brain data.