Computational modelling in neuroscience is gaining in popularity towards investigating neurological and psychiatric disorders. One of the major obstacles in faster progress in this field has been the current state-of-the-art computational platforms and frameworks that struggle to simulate, in terms of time and memory, the complex brain structures and functions. Thus, modelling of neuronal population that are packed in dense spatial clusters and show local synchrony has been a popular methodology towards simulating higher level brain functions recorded via electroencephalogram (EEG); neural mass modelling has been one such paradigm. The drawback in these models of population level dynamics, however, is a lack of correlation with the underlying cellular mechanisms, which is crucial in investigating disease conditions. The neural mass model presented in this work approaches both these issues: first, kinetic models of synaptic information transfer replaces Rall’s alpha function that are traditionally used in these models, thus allowing correlation of model output simulating EEG-like dynamics with lower-level synaptic attributes; second, computational time for this modified approach in neural mass models is faster than the existing traditional approach and up to an order of ten. Here, the objective is to understand the underlying cellular mechanisms of alpha and theta rhythms that are EEG biomarkers in several neurological and psychiatric disorders. A biologically-inspired model of the thalamic Lateral Geniculate Nucleus using the modified neural mass modelling framework is tuned and parameterised to simulate EEG alpha and theta rhythms. The results suggest that low-levels of neurotransmitter concentration in the synaptic cleft along with a reduced GABA-ergic activity from the thalamic interneurons may play a role in alpha to theta band transition, a symptom implicated in several brain disorders. Furthermore, the model validates reports from experimental observations that similar thalamic mechanisms underlie alpha and theta band oscillations. In addition, the model predicts that the GABA-ergic pathways from the thalamic interneurons and the thalamic reticular nucleus may have distinct roles in EEG during cognitive state and state of sleep, and in both healthy and diseased brains.