In the brain, complex information interactions among neurons span several spatial and temporal scales, making it extremely difficult to identify the principles governing neural information processing. In this study, we used computational models to investigate the impact of dendritic morphology and synaptic topology on patterns of neuronal firing. We first constructed Hodgkin-Huxley-type neuron models that possessed dendrites with different morphological features. We then simulated the responses of these neurons to a number of spatiotemporal input patterns. The similarity between neuronal responses to different patterned inputs was effectively evaluated by a novel combination of metric space analysis and multidimensional scaling analyses. The results showed that neurons with different morphological or anatomical features exhibit differences in stimulus-specific temporal encoding and firing reliability. These findings support the idea that in addition to biophysical membrane properties, the dendritic morphology and the synaptic topology of a neuron can play a significant role in neuronal information processing and may directly contribute to various brain functions.