Biologically inspired neural networks are a promising approach to understand the causes and improve the treatments of brain damage. Parkinson's disease is a progressive nervous system disorder that affects mainly movements, speech and cognitive problems. It symptoms cannot be cured, though medications can significantly improve the condition. Among the symptoms, tremor is the only one which remains unaffected by medications and is only responsive to deep-brain stimulation. A simplified, cortico-thalamo-cerebellar model will be simulated with spiking neural networks to evaluate the disease effects under dopamine depletion and connectivity weight changes. Confirming previous findings, striatal dopamine depletion was not found to cause tremor, nor its injection to affect tremor severity. The model showed evidence that parkinsonian weight changes in the pallidal inner feedback loop (GPi-GPe) are responsible of creating a suitable environment for the PD tremor oscillations to rise in the thalamus. Furthermore, both the GPi and the GPe present enhanced maximal activity coherent with muscular co-contraction onsets showing evidence of abnormal basal ganglia firing during re-emergent tremor. These findings may connect abnormal basal ganglia activity to the main parkinsonian motor impairments and may help explaining the beneficial effects of deep-brain stimulation on tremor severity.