Integrated dopaminergic neuronal model with reduced intracellular processes and inhibitory autoreceptors

Dopamine (DA) is an important neurotransmitter for multiple brain functions, and dysfunctions of the dopaminergic system are implicated in neurological and neuropsychiatric disorders. Although the dopaminergic system has been studied at multiple levels, an integrated and efficient computational model that bridges from molecular to neuronal circuit level is still lacking. In this study, the authors aim to develop a realistic yet efficient computational model of a dopaminergic pre-synaptic terminal. They first systematically perturb the variables/substrates of an established computational model of DA synthesis, release and uptake, and based on their relative dynamical timescales and steady-state changes, approximate and reduce the model into two versions: one for simulating hourly timescale, and another for millisecond timescale. They show that the original and reduced models exhibit rather similar steady and perturbed states, whereas the reduced models are more computationally efficient and illuminate the underlying key mechanisms. They then incorporate the reduced fast model into a spiking neuronal model that can realistically simulate the spiking behaviour of dopaminergic neurons. In addition, they successfully include autoreceptor-mediated inhibitory current explicitly in the neuronal model. This integrated computational model provides the first step toward an efficient computational platform for realistic multiscale simulation of dopaminergic systems in in silico neuropharmacology.